陈开明 (Kai Ming Ting)

Professor

National Key Laboratory for Novel Software Technology & School of Artificial Intelligence

Xianlin Campus, Nanjing University

Email: tingkm at nju.edu.cn

特任副研究员/特任助理研究员招聘 Available

Research Interests

·         Isolation kernel and Isolation Distributional Kernel

·         Mass-based similarity

·         Mass estimation and mass-based approaches

·         Ensemble approaches

·         Data stream data mining

·         Machine learning

Short Biography

After receiving his PhD from the University of Sydney, Australia, Kai Ming Ting worked at the University of Waikato (NZ), Deakin University, Monash University and Federation University in Australia. He joined Nanjing University in 2020.

Research grants received include those from National Natural Science Foundation of China, US Air Force of Scientific Research (AFOSR/AOARD), Australian Research Council, Toyota InfoTechnology Center and Australian Institute of Sport.

He is the principal driver of isolation-based methods. The first of its kind is Isolation Forest which is one of the most effective and efficient anomaly detectors created thus far. Since its creation in 2008, it has been widely used by academia as well as industry with close to 10,000 citations recorded by Google Scholar. He is also the principal creator of Isolation Kernel (IK) and Isolation Distributional Kernel (IDK). Shown in AI Journal 2024, IK is the only measure which can break the curse of dimensionality since the inception of the field. This has wide-changing implications on all fields of applications which employ similarity/distance measures. Since its introduction in KDD2020, IDK has significant changed the landscape of data mining methods, enabling anomaly detection, clustering and retrieval tasks to be accomplished more effectively in applications such as Spatial Transcriptomics (in bio-informatics),  trajectories, time series and graphs. The IDK-based methods have been shown to outperform SOTA methods; in addition, they are able to run faster in CPU than deep learning methods running in GPU. 

Qualifications

·         Graduate Certificate of Higher Education - Monash University 2004

·         Ph.D, Basser Department of Computer Science - University of Sydney 1996

·         Master of Computer Science - University of Malaya 1992

·         Bachelor of Electrical Engineering- University of Technology Malaysia 1986

Selected Program Committees

•     Area Chair: International Joint Conference on Artificial Intelligence, 2021

•     Program Co-chairs: The Twelfth Pacific-Asia Conference on Knowledge Discovery and Data Mining, Osaka, Japan, 2008.

•     Tutorial Co-chair: The Eighth Pacific-Asia Conference on Knowledge Discovery and Data Mining, Sydney, Australia, 2004.

•     Senior PC member: AAAI Conference on Artificial Intelligence, 2019,2023.

•     Senior PC member: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2021-2023.

•     Senior PC member: Pacific Asia Conference on Knowledge Discovery and Data Mining, 2016, 2017, 2021.

•     Program committee member (since 2014)

•   KDD 2015-2018: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

•   ICDM 2014-2016, 2018-2020: IEEE International Conference on Data Mining.

•   IJCAI 2017: International Joint Conference on Artificial Intelligence.

•   ECML 2016: European Conference on Machine Learning.

•   PAKDD 2015: Pacific-Asia Conf. on Knowledge Discovery and Data Mining.

•   AISTATS 2021: International Conference on Artificial Intelligence and Statistics.

Tutorial Presentation

·         “Which Anomaly Detector should I use?” in 2018 International Conference on Data Mining.

·         “Mass Estimation: Enabling density-based or distance-based algorithms to do what they cannot do” in 2016 Asian Conference on Machine Learning.

·         “BIG DATA MINING” in Big Data School, 2013 Pacific-Asia Conference on Knowledge Discovery and Data Mining.

Software Downloads

·         Isolation Kernel and Isolation Distributional Kernel

·         Isolation Nearest Neighbour Ensemble

·         Isolation Forest: A fast and effective anomaly detector

·         Mass Estimation and its suite of software

·         Feating: an ensemble that works with SVM

Selected Publications

(Full publication list see here)

  1. Kai Ming Ting, Zong-you Liu, Lei Gong, Hang Zhang, and Ye Zhu (2024). A new distributional treatment for time series anomaly detection. The VLDB Journal: 1-28.
  2. Yang Cao, Ye Zhu, Kai Ming Ting, Flora D. Salim, Hong Xian Li, Luxing Yang, and Gang Li (2024). Detecting Change Intervals with Isolation Distributional Kernel. Journal of Artificial Intelligence Research. 79 : 273-306.
  3. Kai Ming Ting, Takashi Washio, Ye Zhu, Yang Xu, and Kaifeng Zhang (2024). Is it possible to find the single nearest neighbor of a query in high dimensions? Artificial Intelligence. 336: 104206.
  4. Yufan Wang, Zijing Wang,Kai Ming Ting, and Yuanyi Shang (2024). A Principled Distributional Approach to Trajectory Similarity Measurement and its Application to Anomaly Detection. Journal of Artificial Intelligence Research. 79: 865-893.
  5. Ye Zhu, and Kai Ming Ting (2023). Kernel-based clustering via isolation distributional kernel. Information Systems. 117: 102212.
  6. Kai Ming Ting, Takashi Washio, Jonathan Wells, Hang Zhang, and Ye Zhu (2023). Isolation Kernel Estimators. Knowledge and Information Systems. 65(2): 759-787.
  7. Xin Han, Ye Zhu, Kai Ming Ting, and Gang Li (2023). The impact of isolation kernel on agglomerative hierarchical clustering algorithms. Pattern Recognition. 139: 109517.
  8. Kai Ming Ting, Bi-Cun Xu, Takashi Washio, and Zhi-hua Zhou (2023). Isolation Distributional Kernel: A New Tool for Point & Group Anomaly Detection. IEEE Transactions on Knowledge and Data Engineering. 35(3): 2697-2710.
  9. Kai Ming Ting, Jonathan R. Wells, Ye Zhu (2023). Point-Set Kernel Clustering. IEEE Transactions on Knowledge and Data Engineering. 35(5): 5147-5158.
  10. Ye Zhu, Kai Ming Ting, Yuan Jin, and Maia Angelova (2022). Hierarchical clustering that takes advantage of both density-peak and density-connectivity. Information Systems. 103: 101871.
  11. Xiang-yu Song, Sunil Aryal, Kai Ming Ting, Zhen Liu, and Bin He (2022). Spectral–spatial anomaly detection of hyperspectral data based on improved isolation forest. IEEE Transactions on Geoscience and Remote Sensing. 60: 1-16.
  12. Ming Pang, Kai Ming Ting, Peng Zhao, and Zhi-hua Zhou (2022). Improving deep forest by screening. IEEE Transactions on Knowledge and Data Engineering. 9: 4298-4312.
  13. Kai Ming Ting, Jonathan R. Wells, and Takashi Washio (2021). Isolation kernel: the X factor in efficient and effective large scale online kernel learning. Data Mining and Knowledge Discovery. 35(6): 2282-2312.
  14. Ye Zhu, and Kai Ming Ting (2021). Improving the effectiveness and efficiency of stochastic neighbour embedding with isolation kernel. Journal of Artificial Intelligence Research. 71: 667-695.
  15. Ye Zhu, Kai Ming Ting, Mark J. Carman, and Maia Angelova (2021). CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities. Pattern Recognition. 117: 107977.
  16. Ming Pang, Kai Ming Ting, Peng Zhao, Zhi-hua Zhou (2020). Improving deep forest by screening. IEEE Transactions on Knowledge and Data Engineering. 34(9), 4298-4312.
  17. Jonathan R. Wells, Sunil Aryal and Kai Ming Ting (2020). Simple supervised dissimilarity measure: Bolstering iForest-induced similarity with class information without learning. Knowledge and Information Systems. 62(8): 3203-3216.
  18. Sunil Aryal, Kai Ming Ting, Takashi Washio, Gholamreza Haffari (2020). A comparative study of data-dependent approaches without learning in measuring similarities of data objects. Data Mining and Knowledge Discovery. 34: 124–162.
  19. Jonathan R. Wells and Kai Ming Ting (2019). A simple efficient density estimator that enables fast systematic search. Pattern Recognition Letters. 122: 92-98.
  20. Kai Ming Ting, Ye Zhu, Mark James Carman, Yue Zhu, Takashi Washio and Zhi-hua Zhou (2019). Lowest Probability Mass Neighbour Algorithms: Relaxing the metric constraint in distance-based neighbourhood algorithms. Machine Learning. 108(2): 331-376.
  21. Ye Zhu, Kai Ming Ting, Mark James Carman (2018). Grouping points by shared subspaces for effective subspace clustering. Pattern Recognition. 83: 230-244.
  22. Bo Chen, Kai Ming Ting and Takashi Washio (2018). Local Contrast as an effective means to robust clustering against varying densities. Machine Learning. 107: 1621-1645.
  23. Yue Zhu, Kai Ming Ting, Zhi-hua Zhou (2018). Multi-Label Learning with Emerging New Labels. IEEE Transactions on Knowledge and Data Engineering. 30(10): 1901-1912.
  24. Tharindu R. Bandaragoda, Kai Ming Ting, David Albrecht, Fei Tony Liu and Jonathan R. Wells (2018). Isolation-based Anomaly Detection using Nearest Neighbour Ensembles. Computational Intelligence. 34(4): 968-998.
  25. Kai Ming Ting, Takashi Washio, Jonathan R. Wells and Sunil Aryal (2017). Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors. Machine Learning. 106(1): 55-91.
  26. Sunil Aryal, Kai Ming Ting, Takashi Washio, Gholamreza Haffari (2017). Data-dependent dissimilarity measure: an effective alternative to geometric distance measures. Knowledge and Information Systems. 34(4): 968-998.
  27. Mu Xin, Kai Ming Ting and Zhi-hua Zhou (2017). Classification under Streaming Emerging New Classes: A Solution using Completely-random Trees. IEEE Transactions on Knowledge and Data Engineering. 29: 1605-1618.
  28. Guan-song Pang, Kai Ming Ting, David Albrecht, Huidong Jin (2016). ZERO++: Harnessing the power of zero appearances to detect anomalies. Journal of Artificial Intelligence Research. 57: 593-620.
  29. Ye Zhu, Kai Ming Ting, Mark James Carman (2016). Density-ratio based clustering for discovering clusters with varying densities. Pattern Recognition. 60(C): 983-997.
  30. Sunil Aryal and Kai Ming Ting (2016). A generic ensemble approach to estimate multi-dimensional likelihood in Bayesian classifier learning. Computational Intelligence. 32(3): 458-479.
  31. Bo Chen, Kai Ming Ting, Takashi Washio and Gholamreza Haffari (2015). Half-Space Mass: A maximally robust and efficient data depth method. Machine Learning. 100 (2-3), 677-699.
  32. Jonathan R. Wells, Kai Ming Ting and Takashi Washio (2014). LiNearN: A New Approach to Nearest Neighbour Density Estimator. Pattern Recognition. 47(8): 2702-2720. Elsevier.
  33. Kai Ming Ting, Guang-tong Zhou, Fei Tony Liu and Swee Chuan Tan (2013). Mass Estimation. Machine Learning. 90(1): 127-160.
  34. Zhou-yu Fu, Guo-jun Lu, Kai Ming Ting and Deng-sheng Zhang (2013). Learning Sparse Kernel Classifiers for Multi-Instance Classification. IEEE Transactions on Neural Networks and Learning Systems. 24(9): 1377-1389.
  35. Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Fei Tony Liu and Sunil Aryal (2013). DEMass: A New Density Estimator for Big Data. Knowledge and Information Systems. 35(3): 493-524. Springer.
  36. Zhou-yu Fu, Guo-jun Lu, Kai Ming Ting and Deng-sheng Zhang (2013). Efficient Nonlinear Classification Via Low-Rank Regularised Least Squares. Neural Computing and Applications. 22(7-8): 1279-1289. Springer.
  37. Kai Ming Ting, Lian Zhu and Jonathan R. Wells (2013). Local Models—The Key to Boosting Stable Learners Successfully. Computational Intelligence. 29(2): 331-356. Elsevier.
  38. Guang-tong Zhou, Kai Ming Ting, Fei Tony Liu and Yi-long Yin (2012). Relevance Feature Mapping for Content-based Multimedia Information Retrieval. Pattern Recognition. 45: 1707-1720.
  39. Geoffrey I. Webb, Janice R. Boughton, Fei Zheng, Kai Ming Ting and Houssam Salem (2012). Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification. Machine Learning. 86(2):233-272.
  40. Fei Tony Liu, Kai Ming Ting, Yang Yu and Zhi-hua Zhou (2012). Isolation-Based Anomaly Detection. ACM Transactions on Knowledge Discovery from Data. Vol.6, Issue.1, Article No.3:1-39.
  41. Swee Chuan Tan, Kai Ming Ting and Shyh Wei Teng (2011). A General Stochastic Clustering Method for Automatic Cluster. Pattern Recognition. 44(10-11): 2786-2799. Elsevier.
  42. Zhou-yu Fu, Guo-jun Lu, Kai Ming Ting and Deng-sheng Zhang (2011). A Survey of Audio-based Music Classification and Annotation. IEEE Transactions on Multimedia. 14(2): 303-319.
  43. Zhou-yu Fu, Guo-jun Lu, Kai Ming Ting and Deng-sheng Zhang (2011). Music Classification Via the Bag-of-Features Approach. Pattern Recognition Letters. Vol.32, Issue 14. 1768-1777.
  44. Kai Ming Ting, Jonathan R. Wells, Swee Chuan Tan, Shyh Wei Teng and Geoffrey I. Webb (2011). Feature-Subspace Aggregating: Ensembles for Stable and Unstable Learners. Machine Learning. Vol. 82, No. 3, 375-397.
  45. Fei Tony Liu, Kai Ming Ting, Yang Yu and Zhi-hua Zhou (2008). Spectrum of Variable-Random Trees. Journal of Artificial Intelligence Research. 355-384.
  46. Ying Yang, Geoffrey I. Webb, Kevin Korb and Kai Ming Ting (2007). Classifying under computational resource constraints: anytime classification using probabilistic estimators. Machine Learning. Vol.69. No.1. 35-53.
  47. Ying Yang, Geoffrey I. Webb, J. Cerquides, Kevin Korb, Janice R. Boughton and Kai Ming Ting (2007). To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Ensembles. IEEE Transactions on Knowledge and Data Engineering. 19(12): 1652-1665.
  48. Geoffrey I. Webb and Kai Ming Ting (2005). On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions. Machine Learning. 58(1): 25-32. Springer.
  49. Kai Ming Ting and Zi-jian Zheng (2003). A Study of AdaBoost with Naïve Bayesian Classifiers: Weakness and Improvement. Computational Intelligence. Vol. 19, No. 2. 186-200. Wiley-Blackwell Publishing.
  50. Kai Ming Ting (2002). An Instance-Weighting Method to Induce Cost-Sensitive Trees. IEEE Transaction on Knowledge and Data Engineering. 14(3): 659-665.
  51. Kai Ming Ting and Ian H. Witten (1999). Issues in Stacked Generalization. Journal of Artificial Intelligence Research. AI Access Foundation and Morgan Kaufmann Publishers. 10: 271-289.

    52. CONFERENCE PUBLICATIONS

  52. Lei Gong, Hang Zhang, Zongyou Liu, Kai Ming Ting, Yang Cao, and Ye Zhu (2024). Local Subsequence-Based Distribution for Time Series Clustering. Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining . 259-270.
  53. Yuanyi Shang, Kai Ming Ting, Zijing Wang, and Yufan Wang (2024). Distributional Kernel: An Effective and Efficient Means for Trajectory Retrieval. Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining . 271-283.
  54. Zi-jing Wang, Ye Zhu, and Kai Ming Ting (2023). Distribution-Based Trajectory Clustering. In 2023 IEEE International Conference on Data Mining . pp. 1379-1384.
  55. Hang Zhang, Kai-feng Zhang, Kai Ming Ting, and Ye Zhu (2023). Towards a persistence diagram that is robust to noise and varied densities. In Proceedings of the 40th International Conference on Machine Learning. pp. 41952-41972.
  56. Zhong Zhuang, Kai Ming Ting, Guan-song Pang, and Shuai-bin Song (2023). Subgraph Centralization: A Necessary Step for Graph Anomaly Detection. SIAM International Conference on Data Mining . pp. 703-711.
  57. Kai Ming Ting, Zong-you Liu, Hang Zhang, and Ye Zhu (2022). A new distributional treatment for time series and an anomaly detection investigation. Proceedings of the VLDB Endowment 15. 11: 2321-2333.
  58. Xin Han, Ye Zhu, Kai Ming Ting, De-chuan Zhan, and Gang Li (2022). Streaming hierarchical clustering based on point-set kernel.In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. pp. 525-533.
  59. Bi-Cun Xu, Kai Ming Ting, and Yuan Jiang (2021). Isolation graph kernel. In Proceedings of the AAAI Conference on Artificial Intelligence. 35(12): 10487-10495.
  60. Yi-xuan Xu, Ming Pang, Ji Feng, Kai Ming Ting, Yuan Jiang, and Zhi-hua Zhou (2021). Reconstruction-based anomaly detection with completely random forest. In Proceedings of the 2021 SIAM International Conference on Data Mining , pp. 127-135. Society for Industrial and Applied Mathematics.
  61. Kai Ming Ting, Takashi Washio, Jonathan R. Wells, and Hang Zhang (2021). Isolation kernel density estimation. In 2021 IEEE International Conference on Data Mining (ICDM). pp. 619-628. IEEE.
  62. Kai Ming Ting, Bi-Cun Xu, Washio Takashi, Zhi-Hua Zhou (2020). Isolation Distributional Kernel: A new tool for kernel based anomaly detection. Proceedings of The ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 198-206.
  63. Bo Chen, Kai Ming Ting, and Tat-Jun Chin (2020). Anomaly Detection via Neighbourhood Contrast. Proceedings of the 24thPacific-Asia Conference on Knowledge Discovery and Data Mining. 647-659.
  64. Durgesh Samariya, Sunil Aryal, Kai Ming Ting, and Jian-gang Ma (2020). A new effective and efficient measure for outlying aspect mining. Proceedings of the 21st International Conference on Web Information Systems Engineering. 463-474.
  65. Bi-cun Xu, Kai Ming Ting, Zhi-hua Zhou (2019). Isolation Set-Kernel and Its Application to Multi-Instance Learning. Proceedings of The ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 941-949.
  66. Xiao-yu Qin, Kai Ming Ting, Ye Zhu and Vincent Cheng Siong Lee (2019). Nearest-Neighbour-Induced Isolation Similarity and Its Impact on Density-Based Clustering. Proceedings of The Thirty-Third AAAI Conference on Artificial Intelligence. 4755-4762.
  67. Xin-qiang Cai, Peng Zhao, Kai Ming Ting, Xin Mu, Yuan Jiang (2019). Nearest Neighbor Ensembles: An Effective Method for Difficult Problems in Streaming Classification with Emerging New Classes. Proceedings of IEEE International Conference on Data Mining. 970-975.
  68. Kai Ming Ting, Yue Zhu, Zhi-hua Zhou (2018). Isolation Kernel and Its Effect on SVM. Proceedings of The ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2329-2337.
  69. Ming Pang, Peng Zhao, Kai Ming Ting, Zhi-hua Zhou (2018). Improving deep forest by confidence screening. Proceedings of IEEE International Conference on Data Mining. 1194-1199.
  70. Bo Chen and Kai Ming Ting (2018). Neighbourhood Contrast: A better means to detect clusters than density. Proceedings of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining.Part III 22 (pp. 401-412). Springer International Publishing.
  71. Ye Zhu, Kai Ming Ting and Maia Angelova (2018). A Distance Scaling Method to improve density-based clustering. Proceedings of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining. Part III 22 (pp. 389-400). Springer International Publishing.
  72. Ye Zhu, Kai Ming Ting, Zhi-hua Zhou (2017). New class adaptation via instance generation in one-pass class incremental learning. Proceedings of the 17th IEEE International Conference on Data Mining. 1207-1212.
  73. Ye Zhu, Kai Ming Ting, Zhi-hua Zhou (2017). Discover Multiple Novel Labels in Multi-Instance Multi-Label Learning. Proceedings of the 2017 AAAI Conference on Artificial Intelligence. 2977-2984.
  74. Kai Ming Ting, Ye Zhu, Mark James Carman, Yue Zhu, Zhi-hua Zhou (2016). Overcoming Key Weaknesses of Distance-based Neighbourhood Methods using a Data Dependent Dissimilarity Measure. Proceedings of The ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1205-1214.
  75. Ye Zhu, Kai Ming Ting, Zhi-hua Zhou (2016). Multi-Label Learning with Emerging New Labels. Proceedings of the 2016 IEEE International Conference on Data Mining. 1371-1376.
  76. Sunil Aryal, Kai Ming Ting, Gholamreza Haffari and Takashi Washio (2015). Beyond tf-idf and cosine Proceedings of Asia Information Retrieval Societies Conference. 363-368.
  77. Sunil Aryal, Kai Ming Ting, Gholamreza Haffari and Takashi Washio (2014). mp-dissimilarity: A data dependent dissimilarity measure. Proceedings of the 2014 IEEE International Conference on Data Mining. 707-711.
  78. Sunil Aryal, Kai Ming Ting, Jonathan R. Wells and Takashi Washio (2014). Improving iForest with Relative Mass. Proceedings of the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining. 510-521.
  79. Sunil Aryal and Kai Ming Ting (2013). MassBayes: A new generative classifier with multi-dimensional likelihood estimation. Proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining. 136-148, Springer.
  80. Zhou-yu Fu, Guo-jun Lu, Kai Ming Ting and Deng-sheng Zhang (2013). Learning Optimal Cepstral Features for Audio Classification. Proceedings of the International Joint Conference on Artificial Intelligence. 1330-1336.
  81. Zhou-yu Fu, Guo-jun Lu, Kai Ming Ting and Deng-sheng Zhang (2012). Learning Sparse Kernel Classifiers in the Primal. Proceedings of International Workshop on Structural, Syntactical, and Statistical Pattern Recognition. 60-69.
  82. Kai Ming Ting, Takashi Washio, Jonathan R. Wells and Fei Tony Liu (2011). Density Estimation based on Mass. Proceedings of The 11th IEEE International Conference on Data Mining. 715-724.
  83. Swee Chuan Tan, Kai Ming Ting and Fei Tony Liu (2011). Fast Anomaly Detection for Streaming Data. Proceedings of the International Joint Conference on Artificial Intelligence. 1151-1156.
  84. Zhou-yu Fu, Guo-jun Lu, Kai Ming Ting and Deng-sheng Zhang (2011). Building Sparse Support Vector Machines for Multi-Instance Classification. Proceedings of European Conference on Machine Learning. 471-486.
  85. Swee Chuan Tan, Kai Ming Ting and Shyh Wei Teng (2011). Clustering gene expression data using ant-based heuristics. Proceedings of the 2011 IEEE Congress on Evolutionary Computation. 5-8 June 2011. New Orleans, US.
  86. Kai Ming Ting, Guang-Tong Zhou. Fei Tony Liu and Swee Chuan Tan (2010). Mass Estimation and Its Applications. Proceedings of The 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 989-998.
  87. Kai Ming Ting and Jonathan R. Wells (2010). Multi-Dimensional Mass Estimation and Mass-based Clustering. Proceedings of The 10th IEEE International Conference on Data Mining. 511-520.
  88. Fei Tony Liu, Kai Ming Ting and Zhi-hua Zhou (2010). On Detecting Clustered Anomalies using SCiForest. Proceedings of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. 274-290.
  89. Zhou-yu Fu, Guo-jun Lu, Kai Ming Ting and Deng-sheng Zhang (2010). On Feature Combination for Music Classification. Proceedings of International Workshop on Structural, Syntactical & Statistical Pattern Recognition. 453-462.
  90. Fei Tony Liu, Kai Ming Ting and Zhi-hua Zhou (2008). Isolation Forest. Proceedings of the 2008 IEEE International Conference on Data Mining. 413-422. IEEE Computer Society.
  91. Yang Yu, Zhi-hua Zhou and Kai Ming Ting (2007). Cocktail Ensemble for Regression. Proceedings of the 2007 IEEE International Conference on Data Mining. 721-726.
  92. Fei Tony Liu and Kai Ming Ting (2006). Variable Randomness in Decision Tree Ensembles. Proceedings of the Tenth Pacific-Asia Conference on Knowledge Discovery and Data Mining. Lecture Note in Artificial Intelligence (LNAI) 3918. 81-90. Springer-Verlag.
  93. Ying Yang, Geoffrey I. Webb, J. Cerquides, Kevin Korb, Janice R. Boughton and Kai Ming Ting (2006). To Select or To Weigh: A Comparative Study of Model Selection and Model Weighing for SPODE Ensembles. Proceedings of the 17th European Conference on Machine Learning (ECML 2006). Lecture Notes in Computer Science (LNCS) 4212. 533-544. Springer.
  94. Tasadduq Imam, Kai Ming Ting and Joarder Kamruzzaman (2006). z-SVM: An SVM for Improved Classification of Imbalanced Data. Proceedings of the 19th Australian Joint Conference on Artificial Intelligence (AI 2006). Lecture Notes in Computer Science (LNCS) 4304. 264-273. Springer.
  95. Fei Tony Liu, Kai Ming Ting and Wei Fan (2005). Maximizing Tree Diversity by Building Complete-Random Decision Trees. Proceedings of the Ninth Pacific-Asia Conference on Knowledge Discovery and Data Mining. Lecture Note in Artificial Intelligence (LNAI) 3518. 605-610. Berlin: Springer-Verlag.
  96. Kai Ming Ting (2002). Issues in Classifier Evaluation using Optimal Cost Curves. Proceedings of The Nineteenth International Conference on Machine Learning. 642-649. San Francisco: Morgan Kaufmann.
  97. Kai Ming Ting (2000). A Comparative Study of Cost-Sensitive Boosting Algorithms. Proceedings of The Seventeenth International Conference on Machine Learning. 983-990. San Francisco: Morgan Kaufmann.

This is a personal page maintained by the author.

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TemplateEndEditable --><!--[if gte mso 9]><xml>\n <o:shapedefaults v:ext="edit" spidmax="1026"\/>\n<\/xml><![endif]--><!--[if gte mso 9]><xml>\n <o:shapelayout v:ext="edit">\n <o:idmap v:ext="edit" data="1"\/>\n <\/o:shapelayout><\/xml><![endif]-->\n<\/head>\n\n<body lang="EN-AU" link="blue" vlink="blue" style="tab-interval:50.0pt; background-image:url(/_upload/tpl/05/6d/1389/template1389/https:\/\/cdn.hipwallpaper.com\/i\/82\/65\/Phx9f2.jpg)" data_liveedit_tagid="00000000114F5830"><nav style="text-align: center;background-color: #bbdaa1;color: #333;"><a href="/"><h1>伟德国际(Weide·1949)始于英国-The best platform</h1></a></nav>\n\n<div class="WordSection1" style="margin: 1cm 3cm 1cm 3cm;background:tranparent; opacity:1" data_liveedit_tagid="00000000114F5A90">\n\n<table class="MsoNormalTable" border="0" cellpadding="0" width="0" style="\n width:750.0pt;\n mso-cellspacing:3pt;\n mso-yfti-tbllook:1184;\n mso-padding-alt:0cm 8pt 0cm 8pt;\n " data_liveedit_tagid="00000000114F5CF0">\n <tbody data_liveedit_tagid="00000000114F5F50"><tr style="mso-yfti-irow:0;mso-yfti-firstrow:yes" data_liveedit_tagid="00000000114F6080">\n <td width="166" rowspan="2" valign="top" style="width:122.25pt;padding:.75pt .75pt .75pt .75pt; background:transparent" data_liveedit_tagid="00000000114F62E0">\n <p class="MsoNormal" data_liveedit_tagid="000000002AAFCC70"><span style="mso-no-proof:yes" data_liveedit_tagid="000000002AAFD4B0"><!--[if gte vml 1]><v:shapetype\n id="_x0000_t75" coordsize="21600,21600" o:spt="75" o:preferrelative="t"\n path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f">\n <v:stroke joinstyle="miter"\/>\n <v:formulas>\n <v:f eqn="if lineDrawn pixelLineWidth 0"\/>\n <v:f eqn="sum @0 1 0"\/>\n <v:f eqn="sum 0 0 @1"\/>\n <v:f eqn="prod @2 1 2"\/>\n <v:f eqn="prod @3 21600 pixelWidth"\/>\n <v:f eqn="prod @3 21600 pixelHeight"\/>\n <v:f eqn="sum @0 0 1"\/>\n <v:f eqn="prod @6 1 2"\/>\n <v:f eqn="prod @7 21600 pixelWidth"\/>\n <v:f eqn="sum @8 21600 0"\/>\n <v:f eqn="prod @7 21600 pixelHeight"\/>\n <v:f eqn="sum @10 21600 0"\/>\n <\/v:formulas>\n <v:path o:extrusionok="f" gradientshapeok="t" o:connecttype="rect"\/>\n <o:lock v:ext="edit" aspectratio="t"\/>\n <\/v:shapetype><v:shape id="Picture_x0020_3" o:spid="_x0000_i1028" type="#_x0000_t75"\n style=\'width:111.75pt;height:159.75pt;visibility:visible;mso-wrap-style:square\'>\n <v:imagedata src="/_upload/tpl/05/6d/1389/template1389/index-Nanjing_files\/image002.jpg" o:title="2-inch"\/>\n <\/v:shape><![endif]--><!--[if !vml]-->\n <div style="border-radius: 50; background: white" data_liveedit_tagid="000000002AAFE1C0">\n <img width="149" height="213" src="/_upload/tpl/05/6d/1389/template1389/.\/Kai Ming Ting_files\/image003.jpg" v:shapes="Picture_x0020_3 ;border-radius:250px;" data_liveedit_tagid="000000002AAFE420">\n <\/div><!--[endif]--><\/span><o:p data_liveedit_tagid="000000002AAFFA60"><\/o:p><\/p>\n <\/td>\n <td width="800" nowrap="" colspan="2" valign="top" style="width:618.75pt;padding:.75pt .75pt .75pt .75pt;background:transparent;" nosave="" data_liveedit_tagid="00000000114F70B0">\n <h2 data_liveedit_tagid="00000000114F7310"><span lang="ZH-CN" style="font-size:18.0pt;font-family:Cabri;word-spacing:1pt" data_liveedit_tagid="00000000114F7440"> 陈开明(Kai Ming Ting)<\/span><span style="font-size:18.0pt;mso-ascii-font-family:Cabri" times="" new="" data_liveedit_tagid="00000000114F7B60"><o:p data_liveedit_tagid="00000000114F7C90"><\/o:p><\/span><\/h2>\n <h2 data_liveedit_tagid="00000000114F7EF0"><\/h2>\n <p class="MsoNormal; style=" line-height="30px;" data_liveedit_tagid="00000000114F8150">Professor <br data_liveedit_tagid="00000000114F6C10">\n School of Artificial Intelligence<\/p>\n <p class="MsoNormal" data_liveedit_tagid="00000000114F8610"><span style="color:black;\\" data_liveedit_tagid="00000000114F8740">Nanjing\n University, <span class="SpellE" data_liveedit_tagid="00000000114F89A0">Xianlin<\/span> Campus Mailbox 603<\/span><o:p data_liveedit_tagid="00000000114F8E40"><\/o:p><\/p>\n <p class="MsoNormal" data_liveedit_tagid="00000000114F90A0"><span style="color:black;" data_liveedit_tagid="00000000114F91D0">163 <span class="SpellE" data_liveedit_tagid="00000000114F9430">Xianlin<\/span> Avenue, <span class="SpellE" data_liveedit_tagid="00000000114F97C0">Qixia<\/span> District<\/span><o:p data_liveedit_tagid="00000000114F9B50"><\/o:p><\/p>\n <p class="MsoNormal" data_liveedit_tagid="00000000114F9DB0"><span style="color:black;" data_liveedit_tagid="00000000114F9EE0">Nanjing 210023,\n China<\/span> <br data_liveedit_tagid="00000000114F8D30">\n Email: <span class="SpellE" data_liveedit_tagid="00000000114FA3A0">tingkm<\/span> at nju.edu.cn<\/p>\n <\/td>\n <\/tr>\n <tr style="mso-yfti-irow:1;mso-yfti-lastrow:yes" data_liveedit_tagid="00000000114FAAC0">\n <td style="padding:.75pt .75pt .75pt .75pt" data_liveedit_tagid="00000000114FAD20"><\/td>\n <td style="padding:.75pt .75pt .75pt .75pt" data_liveedit_tagid="00000000114FAF80">\n <p class="MsoNormal" data_liveedit_tagid="00000000114FB1E0"><span style="font-size:10.0pt" data_liveedit_tagid="00000000114FB310"><o:p data_liveedit_tagid="00000000114FB7B0"> <\/o:p><\/span><\/p>\n <\/td>\n <\/tr>\n<\/tbody><\/table>\n\n<div style="background:white; margin-top:0.3cm" data_liveedit_tagid="000000001ACFD310">\n<h2 style="margin-top:0cm;margin-right:1cm;margin-bottom:2cm;margin-left:\n1cm;margin-bottom:12pt;background:#d0d0d0;text-align:center;" data_liveedit_tagid="00000000114FC130"><span style="font-size:21.5pt;\nfont-family:" data_liveedit_tagid="00000000114FC260">Research Interests<\/span><span style="font-size:21.5pt;font-family:"Arial",sans-serif;mso-fareast-font-family:\n"Times New Roman";color:#041243" data_liveedit_tagid="00000000114FC4C0"><o:p data_liveedit_tagid="00000000114FC700"><\/o:p><\/span><\/h2>\n\n<p class="MsoNormal" style="mso-margin-top-alt:auto;margin-bottom:3.0pt;\nmargin-left:17.85pt;text-indent:-17.85pt;mso-list:l9 level1 lfo6;tab-stops:\nlist 36.0pt;background:white;margin-left:2cm;" data_liveedit_tagid="00000000114FC960"><!--[if !supportLists]--><span style="font-size:10.0pt;\nmso-bidi-font-size:12.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;\nmso-bidi-font-family:Symbol;color:#222222;" data_liveedit_tagid="00000000114FCBB0"><span style="mso-list:Ignore" data_liveedit_tagid="00000000114FCCE0">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015900010">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:" times="" new="" data_liveedit_tagid="0000000015900BE0">Isolation kernel<o:p data_liveedit_tagid="0000000015900E40"><\/o:p><\/span><\/p>\n\n<p class="MsoNormal" style="mso-margin-top-alt:auto;margin-bottom:3.0pt;\nmargin-left:17.85pt;text-indent:-17.85pt;mso-list:l9 level1 lfo6;tab-stops:\nlist 36.0pt;background:white;margin-left:2cm;" data_liveedit_tagid="00000000159010A0"><!--[if !supportLists]--><span style="font-size:10.0pt;\nmso-bidi-font-size:12.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;\nmso-bidi-font-family:Symbol;color:#222222" data_liveedit_tagid="00000000159012F0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015901420">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="00000000159017B0">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;color:#222222" data_liveedit_tagid="0000000015902380">Mass-based similarity<o:p data_liveedit_tagid="00000000159025E0"><\/o:p><\/span><\/p>\n\n<p class="MsoNormal" style="mso-margin-top-alt:auto;margin-bottom:3.0pt;\nmargin-left:17.85pt;text-indent:-17.85pt;mso-list:l9 level1 lfo6;tab-stops:\nlist 36.0pt;background:white;margin-left:2cm;" data_liveedit_tagid="0000000015902840"><!--[if !supportLists]--><span style="font-size:10.0pt;\nmso-bidi-font-size:12.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;\nmso-bidi-font-family:Symbol;color:#222222" data_liveedit_tagid="0000000015902A90"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015902BC0">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015902F50">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;color:#222222" data_liveedit_tagid="0000000015903B20">Mass estimation and mass-based\napproaches<o:p data_liveedit_tagid="0000000015903D80"><\/o:p><\/span><\/p>\n\n<p class="MsoNormal" style="mso-margin-top-alt:auto;margin-bottom:3.0pt;\nmargin-left:17.85pt;text-indent:-17.85pt;mso-list:l9 level1 lfo6;tab-stops:\nlist 36.0pt;background:white;margin-left:2cm;" data_liveedit_tagid="0000000015903FE0"><!--[if !supportLists]--><span style="font-size:10.0pt;\nmso-bidi-font-size:12.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;\nmso-bidi-font-family:Symbol;color:#222222" data_liveedit_tagid="0000000015904230"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015904360">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="00000000159046F0">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;color:#222222" data_liveedit_tagid="00000000159052C0">Ensemble approaches<o:p data_liveedit_tagid="0000000015905520"><\/o:p><\/span><\/p>\n\n<p class="MsoNormal" style="mso-margin-top-alt:auto;margin-bottom:3.0pt;\nmargin-left:17.85pt;text-indent:-17.85pt;mso-list:l9 level1 lfo6;tab-stops:\nlist 36.0pt;background:white;margin-left:2cm;" data_liveedit_tagid="0000000015905780"><!--[if !supportLists]--><span style="font-size:10.0pt;\nmso-bidi-font-size:12.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;\nmso-bidi-font-family:Symbol;color:#222222" data_liveedit_tagid="00000000159059D0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015905B00">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015905E90">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;color:#222222" data_liveedit_tagid="0000000015906A60">Data stream data mining<o:p data_liveedit_tagid="0000000015906CC0"><\/o:p><\/span><\/p>\n\n<p class="MsoNormal" style="mso-margin-top-alt:auto;margin-bottom:3.0pt;\nmargin-left:17.85pt;text-indent:-17.85pt;mso-list:l9 level1 lfo6;tab-stops:\nlist 36.0pt;background:white;margin-left:2cm;" data_liveedit_tagid="0000000015906F20"><!--[if !supportLists]--><span style="font-size:10.0pt;\nmso-bidi-font-size:12.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;\nmso-bidi-font-family:Symbol;color:#222222" data_liveedit_tagid="0000000015907170"><span style="mso-list:Ignore" data_liveedit_tagid="00000000159072A0">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015907630">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;color:#222222" data_liveedit_tagid="0000000015908200">Machine learning<o:p data_liveedit_tagid="0000000015908460"><\/o:p><\/span><\/p>\n\n<h2 style="margin-top:10.0pt;margin-right:1cm;margin-bottom:0cm;margin-left:\n1cm;margin-bottom:10pt;background:#D1E9E9;font-variant-ligatures: normal;\nfont-variant-caps: normal;orphans: 2;text-align:center;widows: 2;-webkit-text-stroke-width: 0px;\ntext-decoration-style: initial;text-decoration-color: initial;word-spacing:\n0px;padding-top:0cm; padding-bottom:0cm;" data_liveedit_tagid="00000000159086C0"><span style="font-size:21.5pt;font-family:Times New Roman;" data_liveedit_tagid="00000000159087F0">Short\nBiography<o:p data_liveedit_tagid="0000000015908A50"><\/o:p><\/span><\/h2>\n\n<p class="MsoNormal" style="text-align:justify; text-justify:inter-ideograph;;line-height:30px;margin-right:1cm;margin-left:1cm;" data_liveedit_tagid="0000000015908CB0"><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-fareast-font-family:"Times New Roman";\nmso-hansi-font-family:"Times New Roman";mso-hansi-theme-font:major-fareast;\nmso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:major-fareast" data_liveedit_tagid="0000000015908DE0">After\nreceiving his PhD from the University of Sydney, Australia, Kai Ming Ting\nworked at the University of Waikato (NZ)<\/span><span class="GramE" data_liveedit_tagid="0000000015909040"><span style="mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font:major-fareast;\nmso-fareast-font-family:SimSun;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast" data_liveedit_tagid="0000000015909170">,<\/span><span style="mso-ascii-font-family:\n"Times New Roman";mso-ascii-theme-font:major-fareast;mso-fareast-font-family:\n"Times New Roman";mso-hansi-font-family:"Times New Roman";mso-hansi-theme-font:\nmajor-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:major-fareast" data_liveedit_tagid="00000000159093D0">Deakin<\/span><\/span><span style="mso-ascii-font-family:" times="" new="" data_liveedit_tagid="0000000015909630"> University, Monash University and\nFederation University in Australia. He joined Nanjing University in 2020. He\nhad previously held visiting positions at Osaka University, Nanjing University,\nand Chinese University of Hong Kong.<o:p data_liveedit_tagid="0000000015909890"><\/o:p><\/span><\/p>\n\n<p style="text-align:justify; text-justify:inter-ideograph;;line-height:30px;margin-right:1cm;margin-left:1cm;" data_liveedit_tagid="0000000015909AF0"><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;color:#222222" data_liveedit_tagid="0000000015909C20">He co-chaired the Pacific-Asia\nConference on Knowledge Discovery and Data Mining 2008. He has served as a\nsenior member of program committee for AAAI Conference for AI; a member of\nprogram committees for a number of international conferences including ACM\nSIGKDD, IEEE ICDM, ICML and ECML. Research grants received include those from\nUS Air Force of Scientific Research (AFOSR\/AOARD), Australian Research Council,\nToyota <span class="SpellE" data_liveedit_tagid="0000000015909E80">InfoTechnology<\/span> Center and Australian Institute\nof Sport. Awards received include the Runner-up Best Paper Award in 2008 IEEE\nICDM, and the Best Paper Award in 2006 PAKDD. He was an associate editor for\nJournal of Data Mining and Knowledge Discovery 2011-2015.<o:p data_liveedit_tagid="000000001590A210"><\/o:p><\/span><\/p>\n\n<h2 style="margin-top:12.0pt;margin-right:1cm;margin-bottom:0cm;margin-left:\n1cm;margin-bottom:12pt;background:#d0d0d0;font-variant-ligatures: normal;\nfont-variant-caps: normal;orphans: 2;text-align:center;widows: 2;-webkit-text-stroke-width: 0px;\ntext-decoration-style: initial;text-decoration-color: initial;word-spacing:\n0px;line-height:30px" data_liveedit_tagid="000000001590A470"><span style="font-size:21.5pt;font-family:Times New Roman" data_liveedit_tagid="000000001590A5A0">Qualifications<o:p data_liveedit_tagid="000000001590A800"><\/o:p><\/span><\/h2>\n\n<p class="MsoNormal" style="mso-margin-top-alt:auto;margin-bottom:3.0pt;\nmargin-left:17.85pt;text-indent:-17.85pt;mso-list:l1 level1 lfo7;tab-stops:\nlist 36.0pt;background:white;line-height:30px;margin-left:2cm;" data_liveedit_tagid="000000001590AA60"><!--[if !supportLists]--><span style="font-size:10.0pt;\nmso-bidi-font-size:12.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;\nmso-bidi-font-family:Symbol;color:#222222" data_liveedit_tagid="000000001590ACB0"><span style="mso-list:Ignore" data_liveedit_tagid="000000001590ADE0">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="000000001590B170">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;color:#222222" data_liveedit_tagid="000000001590BD40">Graduate Certificate of Higher\nEducation - Monash University 2004<o:p data_liveedit_tagid="000000001590BFA0"><\/o:p><\/span><\/p>\n\n<p class="MsoNormal" style="mso-margin-top-alt:auto;margin-bottom:3.0pt;\nmargin-left:17.85pt;text-indent:-17.85pt;mso-list:l1 level1 lfo7;tab-stops:\nlist 36.0pt;background:white;line-height:30px;margin-left:2cm;" data_liveedit_tagid="000000001590C200"><!--[if !supportLists]--><span style="font-size:10.0pt;\nmso-bidi-font-size:12.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;\nmso-bidi-font-family:Symbol;color:#222222" data_liveedit_tagid="000000001590C450"><span style="mso-list:Ignore" data_liveedit_tagid="000000001590C580">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="000000001590C910">        \n<\/span><\/span><\/span><!--[endif]--><span class="SpellE" data_liveedit_tagid="000000001590D4E0"><span style="mso-ascii-font-family:\n"Times New Roman";mso-ascii-theme-font:major-fareast;mso-hansi-font-family:\n"Times New Roman";mso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;color:#222222" data_liveedit_tagid="000000001590D610">Ph.D<\/span><\/span><span style="mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font:major-fareast;\nmso-hansi-font-family:"Times New Roman";mso-hansi-theme-font:major-fareast;\nmso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:major-fareast;\ncolor:#222222" data_liveedit_tagid="000000001590D870">, <span class="SpellE" data_liveedit_tagid="000000001590DAD0">Basser<\/span> Department of Computer\nScience - University of Sydney 1996<o:p data_liveedit_tagid="000000001590DE60"><\/o:p><\/span><\/p>\n\n<p class="MsoNormal" style="mso-margin-top-alt:auto;margin-bottom:3.0pt;\nmargin-left:17.85pt;text-indent:-17.85pt;mso-list:l1 level1 lfo7;tab-stops:\nlist 36.0pt;background:white;line-height:30px;margin-left:2cm;" data_liveedit_tagid="000000001590E0C0"><!--[if !supportLists]--><span style="font-size:10.0pt;\nmso-bidi-font-size:12.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;\nmso-bidi-font-family:Symbol;color:#222222" data_liveedit_tagid="000000001590E310"><span style="mso-list:Ignore" data_liveedit_tagid="000000001590E440">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="000000001590E7D0">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;color:#222222" data_liveedit_tagid="000000001590F3A0">Master of Computer Science -\nUniversity of Malaya 1992<o:p data_liveedit_tagid="000000001590F600"><\/o:p><\/span><\/p>\n\n<p class="MsoNormal" style="mso-margin-top-alt:auto;margin-bottom:3.0pt;\nmargin-left:17.85pt;text-indent:-17.85pt;mso-list:l1 level1 lfo7;tab-stops:\nlist 36.0pt;background:white;line-height:30px;margin-left:2cm" data_liveedit_tagid="000000001590F860"><!--[if !supportLists]--><span style="font-size:10.0pt;\nmso-bidi-font-size:12.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;\nmso-bidi-font-family:Symbol;color:#222222" data_liveedit_tagid="000000001590FAB0"><span style="mso-list:Ignore" data_liveedit_tagid="000000001590FBE0">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015AC4010">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;color:#222222" data_liveedit_tagid="0000000015AC4BE0">Bachelor of Electrical\nEngineering- University of Technology Malaysia 1986<o:p data_liveedit_tagid="0000000015AC4E40"><\/o:p><\/span><\/p>\n\n<h2 style="margin-top:12.0pt;margin-right:1cm;margin-bottom:0cm;margin-left:\n1cm;margin-bottom:12pt;background:#D1E9E9;font-variant-ligatures: normal;\nfont-variant-caps: normal;orphans: 2;text-align:center;widows: 2;-webkit-text-stroke-width: 0px;\ntext-decoration-style: initial;text-decoration-color: initial;word-spacing:\n0px;" data_liveedit_tagid="0000000015AC50A0"><span style="font-size:21.5pt;font-family:" times="" new="" data_liveedit_tagid="0000000015AC51D0">Selected\nProgram Committees<o:p data_liveedit_tagid="0000000015AC5430"><\/o:p><\/span><\/h2>\n\n<p class="MsoListParagraphCxSpFirst" style="mso-margin-top-alt:auto;margin-bottom:\n6.0pt;margin-left:2cm;mso-add-space:auto;text-indent:-17.85pt;mso-list:\nl6 level2 lfo12" data_liveedit_tagid="0000000015AC5690"><!--[if !supportLists]--><span style="font-family:Symbol;\nmso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol;mso-fareast-language:\nZH-CN" data_liveedit_tagid="0000000015AC58E0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015AC5A10">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015AC5DA0">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast" data_liveedit_tagid="0000000015AC6970">Program Co-chairs: <span style="mso-bidi-font-style:\nitalic" data_liveedit_tagid="0000000015AC6BD0">The Twelfth Pacific-Asia Conference on Knowledge Discovery and Data\nMining<\/span>, Osaka, Japan, 2008.<\/span><span style="mso-ascii-font-family:\n"Times New Roman";mso-ascii-theme-font:major-fareast;mso-fareast-font-family:\n"MS Mincho";mso-hansi-font-family:"Times New Roman";mso-hansi-theme-font:major-fareast;\nmso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:major-fareast;\nmso-fareast-language:ZH-CN" data_liveedit_tagid="0000000015AC6F60"><o:p data_liveedit_tagid="0000000015AC7090"><\/o:p><\/span><\/p>\n\n<p class="MsoListParagraphCxSpMiddle" style="mso-margin-top-alt:auto;margin-bottom:\n6.0pt;margin-left:2cm;mso-add-space:auto;text-indent:-17.85pt;mso-list:\nl6 level2 lfo11" data_liveedit_tagid="0000000015AC72F0"><!--[if !supportLists]--><span class="mediumheading" data_liveedit_tagid="0000000015AC7540"><span style="font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:\nSymbol;mso-fareast-language:ZH-CN" data_liveedit_tagid="0000000015AC7670"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015AC77A0">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015AC7B30">        \n<\/span><\/span><\/span><\/span><!--[endif]--><span class="mediumheading" data_liveedit_tagid="0000000015AC8700"><span style="mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font:major-fareast;\nmso-fareast-font-family:"MS Mincho";mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast" data_liveedit_tagid="0000000015AC8830">Tutorial Co-chair: The Eighth Pacific-Asia\nConference on Knowledge Discovery and Data Mining, Sydney, Australia, 2004.<\/span><\/span><span class="mediumheading" data_liveedit_tagid="0000000015AC8A90"><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-fareast-font-family:"MS Mincho";\nmso-hansi-font-family:"Times New Roman";mso-hansi-theme-font:major-fareast;\nmso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:major-fareast;\nmso-fareast-language:ZH-CN" data_liveedit_tagid="0000000015AC8BC0"><o:p data_liveedit_tagid="0000000015AC8CF0"><\/o:p><\/span><\/span><\/p>\n\n<p class="MsoListParagraphCxSpMiddle" style="mso-margin-top-alt:auto;margin-bottom:\n6.0pt;margin-left:2cm;mso-add-space:auto;text-indent:-17.85pt;mso-list:\nl6 level2 lfo11" data_liveedit_tagid="0000000015AC8F50"><!--[if !supportLists]--><span style="font-family:Symbol;\nmso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol;mso-fareast-language:\nZH-CN" data_liveedit_tagid="0000000015AC91A0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015AC92D0">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015AC9660">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast" data_liveedit_tagid="0000000015ACA230">Senior PC member: <span style="mso-bidi-font-style:\nitalic" data_liveedit_tagid="0000000015ACA490">AAAI Conference on Artificial Intelligence<\/span>, 2019.<\/span><span style="mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font:major-fareast;\nmso-fareast-font-family:"MS Mincho";mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;mso-fareast-language:ZH-CN" data_liveedit_tagid="0000000015ACA820"><o:p data_liveedit_tagid="0000000015ACA950"><\/o:p><\/span><\/p>\n\n<p class="MsoListParagraphCxSpMiddle" style="mso-margin-top-alt:auto;margin-bottom:\n6.0pt;margin-left:2cm;mso-add-space:auto;text-indent:-17.85pt;mso-list:\nl6 level2 lfo11" data_liveedit_tagid="0000000015ACABB0"><!--[if !supportLists]--><span style="font-family:Symbol;\nmso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol;mso-fareast-language:\nZH-CN" data_liveedit_tagid="0000000015ACAE00"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015ACAF30">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015ACB2C0">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast" data_liveedit_tagid="0000000015ACBE90">Meta Reviewer: <span style="mso-bidi-font-style:\nitalic" data_liveedit_tagid="0000000015ACC0F0">Pacific Asia Conference on Knowledge Discovery and Data Mining, 2016, 2017.<\/span><\/span><span style="mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font:major-fareast;\nmso-fareast-font-family:"MS Mincho";mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;mso-fareast-language:ZH-CN" data_liveedit_tagid="0000000015ACC350"><o:p data_liveedit_tagid="0000000015ACC480"><\/o:p><\/span><\/p>\n\n<p class="MsoListParagraphCxSpMiddle" style="mso-margin-top-alt:auto;margin-bottom:\n6.0pt;margin-left:2cm;mso-add-space:auto;text-indent:-17.85pt;mso-list:\nl6 level2 lfo11" data_liveedit_tagid="0000000015ACC6E0"><!--[if !supportLists]--><span style="font-family:Symbol;\nmso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol;mso-fareast-language:\nZH-CN" data_liveedit_tagid="0000000015ACC930"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015ACCA60">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015ACCDF0">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast" data_liveedit_tagid="0000000015ACD9C0">Program committee member (since 2010)<\/span><span style="mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font:major-fareast;\nmso-fareast-font-family:"MS Mincho";mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;mso-fareast-language:ZH-CN" data_liveedit_tagid="0000000015ACDC20"><o:p data_liveedit_tagid="0000000015ACDD50"><\/o:p><\/span><\/p>\n\n<p class="MsoListParagraphCxSpMiddle" style="mso-margin-top-alt:auto;margin-bottom:\n6.0pt;margin-left:2cm;mso-add-space:auto;text-indent:-17.85pt;mso-list:\nl7 level4 lfo10" data_liveedit_tagid="0000000015ACDFB0"><!--[if !supportLists]--><span style="font-family:Symbol;\nmso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol;mso-fareast-language:\nZH-CN" data_liveedit_tagid="0000000015ACE200"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015ACE330">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015ACE6C0">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-fareast-font-family:"MS Mincho";\nmso-hansi-font-family:"Times New Roman";mso-hansi-theme-font:major-fareast;\nmso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:major-fareast;\nmso-fareast-language:ZH-CN" data_liveedit_tagid="0000000015ACF290">KDD 2010, 2015-2018: <\/span><span style="mso-ascii-font-family:\n"Times New Roman";mso-ascii-theme-font:major-fareast;mso-hansi-font-family:\n"Times New Roman";mso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;mso-bidi-font-style:italic" data_liveedit_tagid="0000000015ACF4F0">ACM SIGKDD\nInternational Conference on Knowledge Discovery and Data Mining<\/span><span style="mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font:major-fareast;\nmso-hansi-font-family:"Times New Roman";mso-hansi-theme-font:major-fareast;\nmso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:major-fareast" data_liveedit_tagid="0000000015ACF750">.<\/span><span style="mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font:major-fareast;\nmso-fareast-font-family:"MS Mincho";mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;mso-fareast-language:ZH-CN" data_liveedit_tagid="0000000015ACF9B0"><o:p data_liveedit_tagid="0000000015ACFAE0"><\/o:p><\/span><\/p>\n\n<p class="MsoListParagraphCxSpMiddle" style="mso-margin-top-alt:auto;margin-bottom:\n6.0pt;margin-left:2cm;mso-add-space:auto;text-indent:-17.85pt;mso-list:\nl7 level4 lfo10" data_liveedit_tagid="0000000015ACFD40"><!--[if !supportLists]--><span style="font-family:Symbol;\nmso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol;mso-fareast-language:\nZH-CN" data_liveedit_tagid="0000000015ACFF90"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015AD00C0">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015AD0450">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast" data_liveedit_tagid="0000000015AD1020">ICDM 2010-2011, 2014-2016, 2018-2019: <span style="mso-bidi-font-style:italic" data_liveedit_tagid="0000000015AD1280">IEEE International Conference on Data\nMining.<\/span><\/span><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-fareast-font-family:"MS Mincho";\nmso-hansi-font-family:"Times New Roman";mso-hansi-theme-font:major-fareast;\nmso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:major-fareast;\nmso-fareast-language:ZH-CN" data_liveedit_tagid="0000000015AD14E0"><o:p data_liveedit_tagid="0000000015AD1610"><\/o:p><\/span><\/p>\n\n<p class="MsoListParagraphCxSpMiddle" style="mso-margin-top-alt:auto;margin-bottom:\n6.0pt;margin-left:2cm;so-add-space:auto;text-indent:-17.85pt;mso-list:\nl7 level4 lfo10" data_liveedit_tagid="0000000015AD1870"><!--[if !supportLists]--><span style="font-family:Symbol;\nmso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol" data_liveedit_tagid="0000000015AD1AC0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015AD1BF0">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015AD1F80">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;mso-bidi-font-style:italic" data_liveedit_tagid="0000000015AD2B50">IJCAI 2017:\nInternational Joint Conference on Artificial Intelligence.<\/span><span style="mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font:major-fareast;\nmso-fareast-font-family:"MS Mincho";mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast" data_liveedit_tagid="0000000015AD2DB0"><o:p data_liveedit_tagid="0000000015AD2EE0"><\/o:p><\/span><\/p>\n\n<p class="MsoListParagraphCxSpMiddle" style="mso-margin-top-alt:auto;margin-bottom:\n6.0pt;margin-left:2cm;mso-add-space:auto;text-indent:-17.85pt;mso-list:\nl7 level4 lfo10" data_liveedit_tagid="0000000015AD3140"><!--[if !supportLists]--><span style="font-family:Symbol;\nmso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol" data_liveedit_tagid="0000000015AD3390"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015AD34C0">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015AD3850">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast" data_liveedit_tagid="0000000015AD44D0">ECML 2016: <span style="mso-bidi-font-style:\nitalic" data_liveedit_tagid="0000000015AD4730">European Conference on Machine Learning.<\/span> <\/span><span style="mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font:major-fareast;\nmso-fareast-font-family:"MS Mincho";mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast" data_liveedit_tagid="0000000015AD4AC0"><o:p data_liveedit_tagid="0000000015AD4BF0"><\/o:p><\/span><\/p>\n\n<p class="MsoListParagraphCxSpMiddle" style="mso-margin-top-alt:auto;margin-bottom:\n6.0pt;margin-left:2cm;mso-add-space:auto;text-indent:-17.85pt;mso-list:\nl7 level4 lfo10" data_liveedit_tagid="0000000015AD4E50"><!--[if !supportLists]--><span style="font-family:Symbol;\nmso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol;mso-fareast-language:\nZH-CN" data_liveedit_tagid="0000000015AD50A0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015AD51D0">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015AD5560">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast" data_liveedit_tagid="0000000015AD6130">ICML 2010: <span style="mso-bidi-font-style:\nitalic" data_liveedit_tagid="0000000015AD6390">International Conference on Machine Learni<\/span>ng. <\/span><span style="mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font:major-fareast;\nmso-fareast-font-family:"MS Mincho";mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;mso-fareast-language:ZH-CN" data_liveedit_tagid="0000000015AD6720"><o:p data_liveedit_tagid="0000000015AD6850"><\/o:p><\/span><\/p>\n\n<p class="MsoListParagraphCxSpLast" style="mso-margin-top-alt:auto;margin-bottom:\n6.0pt;margin-left:2cm;mso-add-space:auto;text-indent:-17.85pt;mso-list:\nl7 level4 lfo10" data_liveedit_tagid="0000000015AD6AB0"><!--[if !supportLists]--><span style="font-family:Symbol;\nmso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol;mso-fareast-language:\nZH-CN" data_liveedit_tagid="0000000015AD6D00"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015AD6E30">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015AD71C0">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast" data_liveedit_tagid="0000000015AD7D90">PAKDD 2015: <\/span><span class="mediumheading" data_liveedit_tagid="0000000015AD7FF0"><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-fareast-font-family:"MS Mincho";\nmso-hansi-font-family:"Times New Roman";mso-hansi-theme-font:major-fareast;\nmso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:major-fareast" data_liveedit_tagid="0000000015AD8120">Pacific-Asia\nConf. on Knowledge Discovery and Data Mining.<\/span><\/span><span style="mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font:major-fareast;\nmso-fareast-font-family:"MS Mincho";mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;mso-fareast-language:ZH-CN" data_liveedit_tagid="0000000015AD8380"><o:p data_liveedit_tagid="0000000015AD84B0"><\/o:p><\/span><\/p>\n\n<h2 style="margin-top:12.0pt;margin-right:1cm;margin-bottom:6.0pt;margin-left:\n1cm;background:#d0d0d0;text-align:center;" data_liveedit_tagid="0000000015AD8710"><span style="font-size:21.5pt;font-family:" times="" new="" data_liveedit_tagid="0000000015AD8840">Tutorial Presentation<o:p data_liveedit_tagid="0000000015AD8AA0"><\/o:p><\/span><\/h2>\n\n<p class="MsoListParagraphCxSpFirst" style="mso-margin-top-alt:auto;margin-bottom:\n3.0pt;margin-left:2cm;mso-add-space:auto;text-align:justify;text-justify:\ninter-ideograph;text-indent:-17.85pt;mso-list:l8 level1 lfo12" data_liveedit_tagid="0000000015AD8D00"><!--[if !supportLists]--><span style="font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:\nSymbol" data_liveedit_tagid="0000000015AD8F50"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015AD9080">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015AD9410">        \n<\/span><\/span><\/span><!--[endif]-->“<a href="/_upload/tpl/05/6d/1389/template1389/https:\/\/federation.edu.au\/__data\/assets\/pdf_file\/0011\/443666\/ICDM2018-Tutorial-Final.pdf" data_liveedit_tagid="0000000015ADA240">Which\nAnomaly Detector should I use?<\/a>” in 2018 International Conference on Data\nMining.<o:p data_liveedit_tagid="0000000015ADA5D0"><\/o:p><\/p>\n\n<p class="MsoListParagraphCxSpMiddle" style="mso-margin-top-alt:auto;margin-bottom:\n3.0pt;margin-left:17.85pt;mso-add-space:auto;text-align:justify;text-justify:\ninter-ideograph;text-indent:-17.85pt;mso-list:l8 level1 lfo12" data_liveedit_tagid="0000000015ADA830"><!--[if !supportLists]--><span style="font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:\nSymbol" data_liveedit_tagid="0000000015ADAA80"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015ADABB0">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015ADAF40">        \n<\/span><\/span><\/span><!--[endif]-->“Mass Estimation: Enabling density-based or\ndistance-based algorithms to do what they cannot do” in 2016 Asian Conference\non Machine Learning.<\/p>\n\n<p class="MsoListParagraphCxSpLast" style="mso-margin-top-alt:auto;margin-bottom:\n3.0pt;margin-left:17.85pt;mso-add-space:auto;text-align:justify;text-justify:\ninter-ideograph;text-indent:-17.85pt;mso-list:l8 level1 lfo12" data_liveedit_tagid="0000000015ADC100"><!--[if !supportLists]--><span style="font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:\nSymbol" data_liveedit_tagid="0000000015ADC350"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015ADC480">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015ADC810">        \n<\/span><\/span><\/span><!--[endif]-->“BIG DATA MINING” in Big Data School, <span style="mso-bidi-font-style:italic" data_liveedit_tagid="0000000015ADD8A0">2013 Pacific-Asia Conference on Knowledge\nDiscovery and Data Mining.<\/span><\/p>\n\n<h2 style="margin-top:12.0pt;margin-right:0cm;margin-bottom:0cm;margin-left:\n0cm;margin-bottom:.0001pt;background:#D1E9E9;text-decoration:underline" data_liveedit_tagid="0000000015ADDC30"><span style="font-size:21.5pt;\nfont-family:"Arial",sans-serif;color:#041243" data_liveedit_tagid="0000000015ADDD60">Software downloads<\/span><span style="font-size:21.5pt;font-family:"Arial",sans-serif;mso-fareast-font-family:\n"Times New Roman";color:#041243" data_liveedit_tagid="0000000015ADDFC0"><o:p data_liveedit_tagid="0000000015ADE0F0"><\/o:p><\/span><\/h2>\n\n<p class="MsoNormal" style="mso-margin-top-alt:auto;margin-bottom:3.0pt;\nmargin-left:17.85pt;text-indent:-17.85pt;mso-list:l0 level1 lfo9;tab-stops:\nlist 36.0pt;background:white" data_liveedit_tagid="0000000015ADE350"><!--[if !supportLists]--><span style="font-size:10.0pt;\nmso-bidi-font-size:12.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;\nmso-bidi-font-family:Symbol;color:#222222" data_liveedit_tagid="0000000015ADE5A0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015ADE6D0">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015ADEA60">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;color:#222222" data_liveedit_tagid="0000000015ADF630"><a href="/_upload/tpl/05/6d/1389/template1389/https:\/\/github.com\/BicunXu\/Isolation_Kernels" style="outline: none" data_liveedit_tagid="0000000015ADF760"><span style="color:#004C97" data_liveedit_tagid="0000000015ADF890">Isolation Kernel: A similarity measure which is\ninfluenced by data distribution of a given dataset<\/span><\/a><o:p data_liveedit_tagid="0000000015ADFAF0"><\/o:p><\/span><\/p>\n\n<p class="MsoNormal" style="mso-margin-top-alt:auto;margin-bottom:3.0pt;\nmargin-left:17.85pt;text-indent:-17.85pt;mso-list:l0 level1 lfo9;tab-stops:\nlist 36.0pt;background:white" data_liveedit_tagid="0000000015ADFD50"><!--[if !supportLists]--><span style="font-size:10.0pt;\nmso-bidi-font-size:12.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;\nmso-bidi-font-family:Symbol;color:#222222" data_liveedit_tagid="0000000015ADFFA0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015AE00D0">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015AE0460">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;color:#222222" data_liveedit_tagid="0000000015AE1030"><a href="/_upload/tpl/05/6d/1389/template1389/https:\/\/github.com\/tharindurb\/iNNE" style="outline: none" data_liveedit_tagid="0000000015AE1160"><span style="color:#004C97" data_liveedit_tagid="0000000015AE1290">Isolation Nearest Neighbour Ensemble\n<\/span><\/a><o:p data_liveedit_tagid="0000000015AE14F0"><\/o:p><\/span><\/p>\n\n\n<p class="MsoNormal" style="mso-margin-top-alt:auto;margin-bottom:3.0pt;\nmargin-left:17.85pt;text-indent:-17.85pt;mso-list:l0 level1 lfo9;tab-stops:\nlist 36.0pt;background:white" data_liveedit_tagid="0000000015AE1750"><!--[if !supportLists]--><span style="font-size:10.0pt;\nmso-bidi-font-size:12.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;\nmso-bidi-font-family:Symbol;color:#222222" data_liveedit_tagid="0000000015AE19A0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015AE1AD0">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015AE1E60">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;color:#222222" data_liveedit_tagid="0000000015AE2A30"><a href="/_upload/tpl/05/6d/1389/template1389/https:\/\/sourceforge.net\/projects\/iforest\" style="outline: none" data_liveedit_tagid="0000000015AE2B60"><span style="color:#004C97" data_liveedit_tagid="0000000015AE2C90">Isolation Forest: A fast and effective anomaly detector<\/span><\/a><o:p data_liveedit_tagid="0000000015AE2EF0"><\/o:p><\/span><\/p>\n\n<p class="MsoNormal" style="mso-margin-top-alt:auto;margin-bottom:3.0pt;\nmargin-left:17.85pt;text-indent:-17.85pt;mso-list:l0 level1 lfo9;tab-stops:\nlist 36.0pt;background:white" data_liveedit_tagid="0000000015AE3150"><!--[if !supportLists]--><span style="font-size:10.0pt;\nmso-bidi-font-size:12.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;\nmso-bidi-font-family:Symbol;color:#222222" data_liveedit_tagid="0000000015AE33A0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015AE34D0">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015AE3860">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;color:#222222" data_liveedit_tagid="0000000015B4A4C0"><a href="/_upload/tpl/05/6d/1389/template1389/https:\/\/sourceforge.net\/projects\/mass-estimation\" style="outline: none" data_liveedit_tagid="0000000015B4A5F0"><span style="color:#004C97" data_liveedit_tagid="0000000015B4A720">Mass Estimation and its suite of software<\/span><\/a><o:p data_liveedit_tagid="0000000015B4A980"><\/o:p><\/span><\/p>\n\n<p class="MsoNormal" style="mso-margin-top-alt:auto;margin-bottom:3.0pt;\nmargin-left:17.85pt;text-indent:-17.85pt;mso-list:l0 level1 lfo9;tab-stops:\nlist 36.0pt;background:white" data_liveedit_tagid="0000000015B4ABE0"><!--[if !supportLists]--><span style="font-size:10.0pt;\nmso-bidi-font-size:12.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;\nmso-bidi-font-family:Symbol;color:#222222" data_liveedit_tagid="0000000015B4AE30"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015B4AF60">·<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015B4B2F0">        \n<\/span><\/span><\/span><!--[endif]--><span style="mso-ascii-font-family:"Times New Roman";\nmso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman";\nmso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman";\nmso-bidi-theme-font:major-fareast;color:#222222" data_liveedit_tagid="0000000015B4BEC0"><a href="/_upload/tpl/05/6d/1389/template1389/https:\/\/sourceforge.net\/projects\/feating\" style="outline: none" data_liveedit_tagid="0000000015B4BFF0"><span class="SpellE" data_liveedit_tagid="0000000015B4C120"><span style="color:#004C97" data_liveedit_tagid="0000000015B4C250">Feating<\/span><\/span><span style="color:#004C97" data_liveedit_tagid="0000000015B4C4B0">: an ensemble that works with SVM<\/span><\/a><o:p data_liveedit_tagid="0000000015B4C710"><\/o:p><\/span><\/p>\n\n<h2 style="margin-top:12.0pt;margin-right:0cm;margin-bottom:6.0pt;margin-left:\n0cm;background:#D1E9E9;" data_liveedit_tagid="0000000015B4C970"><span style="font-size:21.5pt;font-family:"Arial",sans-serif;\ncolor:#041243" data_liveedit_tagid="0000000015B4CAA0">Selected Publications<o:p data_liveedit_tagid="0000000015B4CD00"><\/o:p><\/span><\/h2>\n\n<!--publication-->\n\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.1pt;margin-bottom:3.0pt;\nmargin-left:0cm;mso-layout-grid-align:none" data_liveedit_tagid="0000000015B4D1B0">(Full publication list at <a href="/_upload/tpl/05/6d/1389/template1389/http:\/\/dblp.uni-trier.de\/pers\/hd\/t\/Ting:Kai_Ming" data_liveedit_tagid="0000000015B4D410">http:\/\/dblp.uni-trier.de\/pers\/hd\/t\/Ting:Kai_Ming<\/a>)<o:p data_liveedit_tagid="0000000015B4D7A0"><\/o:p><\/p>\n\n<!--1-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.1pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015B4DC50"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B4DEA0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015B4DFD0">1.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015B4E230">       \n<\/span><\/span><\/span><!--[endif]--><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B4EE00">Sunil Aryal, Kai Ming <\/span><b data_liveedit_tagid="0000000015B4F060">Ting<\/b>, Takashi Washio, and Gholamreza Haffari <span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B4F3F0"> (2020). A comparative study of data-dependent approaches without learning in measuring similarities of data objects. <\/span><i style="mso-bidi-font-style:\nnormal" data_liveedit_tagid="0000000015B4F650"><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B4F780">Data mining and knowledge discovery.<\/span><\/i><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B4F9E0"> Vol.34, No.1, 124–162. <\/span><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B4FEA0"><o:p data_liveedit_tagid="0000000015B4FFD0"><\/o:p><\/span><\/p>\n\n<!--2-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.1pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015B50480"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B506D0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015B50800">2.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015B50A60">       \n<\/span><\/span><\/span><!--[endif]--><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B51630">Jonathan R Wells, Sunil Aryal, and Kai Ming <\/span><b data_liveedit_tagid="0000000015B51890">Ting<\/b> <span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B51C20"> (2020). Simple supervised dissimilarity measure: Bolstering iforest-induced similarity with class information without learning. <\/span><i style="mso-bidi-font-style:\nnormal" data_liveedit_tagid="0000000015B51E80"><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B51FB0">Knowledge and\nInformation Systems,<\/span><\/i><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B52210"> 1-14. <\/span><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B52470"><o:p data_liveedit_tagid="0000000015B525A0"><\/o:p><\/span><\/p>\n\n<!--3-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.1pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015B52A50"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B52CA0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015B52DD0">3.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015B53030">       \n<\/span><\/span><\/span><!--[endif]--><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B53C00">Bo Chen, Kai Ming <\/span><b data_liveedit_tagid="0000000015B53E60">Ting<\/b>, and Tat-Jun Chin <span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B541F0"> (2020). Anomaly detection via neighbourhood contrast. <\/span><i style="mso-bidi-font-style:\nnormal" data_liveedit_tagid="0000000015B54450"><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B54580">Pacific-Asia Conference on Knowledge Discovery and Data Mining.<\/span><\/i><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B547E0"> 647-659, Springer. <\/span><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B54A40"><o:p data_liveedit_tagid="0000000015B54B70"><\/o:p><\/span><\/p>\n\n<!--4-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.1pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015B55020"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B55270"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015B553A0">4.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015B55600">       \n<\/span><\/span><\/span><!--[endif]--><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B561D0">Kai Ming <\/span><b data_liveedit_tagid="0000000015B56430">Ting<\/b>, Jonathan R Wells, and Ye Zhu <span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B567C0"> (2020). Clustering based on point-set kernel. <\/span><i style="mso-bidi-font-style:\nnormal" data_liveedit_tagid="0000000015B56A20"><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B56B50">arXiv\npreprint arXiv:2002.05815.<\/span><\/i><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B56DB0">\n\n<!--5-->\n<\/span><\/p><p class="MsoNormal" style="margin-top:0cm;margin-right:3.1pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015B57260"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B574B0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015B575E0">5.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015B57840">       \n<\/span><\/span><\/span><!--[endif]--><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B58410">Durgesh Samariya, Kai Ming <\/span><b data_liveedit_tagid="0000000015B58670">Ting<\/b>, and Sunil Aryal <span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B58A00"> (2020). A new effective and efficient measure\nfor outlying aspect mining. <\/span><i style="mso-bidi-font-style:\nnormal" data_liveedit_tagid="0000000015B58C60"><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B58D90">arXiv preprint arXiv:2004.13550.<\/span><\/i><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B58FF0"> \n \n<!--6-->\n<\/span><\/p><p class="MsoNormal" style="margin-top:0cm;margin-right:3.1pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015B594A0"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B596F0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015B59820">6.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015B59A80">       \n<\/span><\/span><\/span><!--[endif]--><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B6C720">Kai\nMing <\/span><b data_liveedit_tagid="0000000015B6C980">Ting<\/b><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B6CBE0">, Ye Zhu, Mark\nJames Carman, Yue Zhu, <\/span><span style="mso-fareast-font-family:"Times New Roman";\nmso-fareast-language:EN-AU;mso-bidi-font-weight:bold" data_liveedit_tagid="0000000015B6CE40">Takashi Washio and\nZhi-Hua Zhou<\/span><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B6D0A0"> (2019). Lowest\nProbability Mass Neighbour Algorithms: Relaxing the metric constraint in\ndistance-based neighbourhood algorithms. <\/span><i style="mso-bidi-font-style:\nnormal" data_liveedit_tagid="0000000015B6D300"><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B6D430">Machine\nLearning.<\/span><\/i><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B6D690"> Vol.\n108, Issue 2, 331-376. <\/span><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B6D8F0"><o:p data_liveedit_tagid="0000000015B6DA20"><\/o:p><\/span><\/p>\n\n \n<!--7-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.1pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015B6DED0"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B6E120"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015B6E250">7.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015B6E4B0">       \n<\/span><\/span><\/span><!--[endif]--><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B6F080">Ye\nZhu, Kai Ming <\/span><b data_liveedit_tagid="0000000015B6F2E0">Ting<\/b><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B6F540">,\nMark James Carman (2018). Grouping points by shared subspaces for effective\nsubspace clustering. <i style="mso-bidi-font-style:normal" data_liveedit_tagid="0000000015B6F7A0">Pattern Recognition.<\/i>\nV<\/span>ol 83<span style="color:#505050" data_liveedit_tagid="0000000015B6FC60">, <\/span>2018, Pages 230-244.<span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B6FFF0"><o:p data_liveedit_tagid="0000000015B70120"><\/o:p><\/span><\/p>\n\n<!--8-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.1pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015B705D0"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B70820"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015B70950">8.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015B70BB0">       \n<\/span><\/span><\/span><!--[endif]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B71780">Bo\nChen, <\/span><span style="mso-fareast-font-family:"Times New Roman";mso-fareast-language:\nEN-AU;mso-bidi-font-weight:bold" data_liveedit_tagid="0000000015B719E0">Kai Ming <b data_liveedit_tagid="0000000015B71C40">Ting<\/b> and Takashi Washio<\/span><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B71FD0"> (2018).<span style="mso-spacerun:yes" data_liveedit_tagid="0000000015B72230">  <\/span>Local Contrast as an effective means to\nrobust clustering against varying densities. <i style="mso-bidi-font-style:\nnormal" data_liveedit_tagid="0000000015B726F0">Machine Learning<\/i>, https:\/\/doi.org\/10.1007\/s10994-017-5693-x.<\/span><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B72A80"><o:p data_liveedit_tagid="0000000015B72BB0"><\/o:p><\/span><\/p>\n\n<!--9-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.1pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015B73060"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B732B0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015B733E0">9.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015B73640">       \n<\/span><\/span><\/span><!--[endif]-->Yue Zhu, Kai Ming <b data_liveedit_tagid="0000000015B74340">Ting<\/b>, Zhi-Hua Zhou\n(2018)<span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B746D0">.<\/span> Multi-Label\nLearning with Emerging New Labels. <i style="mso-bidi-font-style:normal" data_liveedit_tagid="0000000015B74A60">IEEE\nTransactions on Knowledge and Data Engineering<\/i>, Vol 30, Issue 10,\n1901-1912, https:\/\/doi.org\/10.1109\/TKDE.2018.2810872.<span style="mso-fareast-font-family:\nSimSun" data_liveedit_tagid="0000000015B74DF0"><o:p data_liveedit_tagid="0000000015B74F20"><\/o:p><\/span><\/p>\n\n<!--10-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.1pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015B753D0"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B75620"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015B75750">10.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015B759B0">       \n<\/span><\/span><\/span><!--[endif]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B76580">Tharindu\nR. Bandaragoda, <\/span>Kai Ming <b style="mso-bidi-font-weight:normal" data_liveedit_tagid="0000000015B76910">Ting<\/b>,\nDavid Albrecht, <span class="SpellE" data_liveedit_tagid="0000000015B76CA0"><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B76DD0">Fei<\/span><\/span><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B77030"> Tony <\/span><span lang="PL" style="mso-fareast-font-family:"Times New Roman";mso-ansi-language:PL" data_liveedit_tagid="0000000015B77290">Liu and <\/span><span style="mso-fareast-font-family:"Times New Roman";mso-bidi-font-weight:bold" data_liveedit_tagid="0000000015B774F0">Jonathan\nR. Wells (2018). <\/span>Isolation-based Anomaly Detection using Nearest Neighbour\nEnsembles. <i style="mso-bidi-font-style:normal" data_liveedit_tagid="0000000015B77880">Computational Intelligence<\/i>.\n<span class="SpellE" data_liveedit_tagid="0000000015B77C10">Doi<\/span>:<span class="article-headermeta-info-data" data_liveedit_tagid="0000000015B77FA0"><span lang="EN" style="mso-ansi-language:EN" data_liveedit_tagid="0000000015B780D0">10.1111\/coin.12156<\/span><\/span><span lang="EN" style="mso-ansi-language:EN" data_liveedit_tagid="0000000015B78330">.<\/span><span style="mso-fareast-font-family:\nSimSun" data_liveedit_tagid="0000000015B78590"><o:p data_liveedit_tagid="0000000015B786C0"><\/o:p><\/span><\/p>\n\n<!--11-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.1pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015B78B70"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B78DC0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015B78EF0">11.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015B79150">       \n<\/span><\/span><\/span><!--[endif]--><span style="mso-fareast-font-family:SimSun;\nmso-fareast-language:EN-AU;mso-bidi-font-weight:bold" data_liveedit_tagid="0000000015B79D20">Kai Ming <b data_liveedit_tagid="0000000015B79F80">Ting<\/b>,Takashi Washio<\/span><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B7A310">, <\/span><span style="mso-fareast-font-family:SimSun;mso-fareast-language:EN-AU;mso-bidi-font-weight:\nbold" data_liveedit_tagid="0000000015B7A570">Jonathan R. Wells and Sunil Aryal (2017). <\/span><span lang="EN" style="mso-ansi-language:EN" data_liveedit_tagid="0000000015B7A7D0">Defying the gravity of learning curve: a\ncharacteristic of nearest <span class="SpellE" data_liveedit_tagid="0000000015B7AA30">neighbour<\/span> anomaly\ndetectors. <i style="mso-bidi-font-style:normal" data_liveedit_tagid="0000000015B7ADC0">Machine Learning<\/i><\/span><span lang="EN" style="mso-fareast-font-family:SimSun;mso-ansi-language:EN" data_liveedit_tagid="0000000015B7B020">.<\/span><span lang="EN" style="mso-ansi-language:EN" data_liveedit_tagid="0000000015B7B280"> Vol 106, Issue 1, 55<\/span>-91. <span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B7B610"><o:p data_liveedit_tagid="0000000015B7B740"><\/o:p><\/span><\/p>\n\n<!--12-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.1pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015B7BBF0"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B7BE40"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015B9E010">12.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015B9E270">       \n<\/span><\/span><\/span><!--[endif]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B9EE40">Sunil\nAryal, Kai Ming <b style="mso-bidi-font-weight:normal" data_liveedit_tagid="0000000015B9F0A0">Ting<\/b>, Takashi\nWashio, Gholamreza Haffari (2017). Data-dependent dissimilarity measure: an\neffective alternative to geometric distance measures. <i style="mso-bidi-font-style:\nnormal" data_liveedit_tagid="0000000015B9F430">Knowledge and Information Systems<\/i>. <\/span><span style="font-size:\n11.0pt;mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015B9F7C0">Doi<span class="GramE" data_liveedit_tagid="0000000015B9FA20">:10.1007<\/span>\/s10115-017-1046-0.<\/span><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015B9FDB0"><o:p data_liveedit_tagid="0000000015B9FEE0"><\/o:p><\/span><\/p>\n\n<!--13-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.1pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015BA0390"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015BA05E0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015BA0710">13.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015BA0970">       \n<\/span><\/span><\/span><!--[endif]-->Xin Mu, Kai Ming <b data_liveedit_tagid="0000000015BA1670">Ting<\/b> and Zhi-Hua Zhou\n(2017). Classification under Streaming Emerging New Classes: A Solution using <span class="GramE" data_liveedit_tagid="0000000015BA1A00">Completely-random<\/span> Trees. <i style="mso-bidi-font-style:normal" data_liveedit_tagid="0000000015BA1D90">IEEE\nTransactions on Knowledge and Data Engineering<\/i>, Vol 29, 1605-1618.<o:p data_liveedit_tagid="0000000015BA2120"><\/o:p><\/p>\n\n<!--14-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.1pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015BA25D0"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015BA2820"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015BA2950">14.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015BA2BB0">       \n<\/span><\/span><\/span><!--[endif]--><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015BA3780">Guansong\nPang, <\/span><span style="mso-fareast-language:EN-AU;mso-bidi-font-weight:bold" data_liveedit_tagid="0000000015BA39E0">Kai\nMing <b data_liveedit_tagid="0000000015BA3C40">Ting<\/b><\/span><span style="mso-fareast-font-family:"Times New Roman";\nmso-fareast-language:EN-AU;mso-bidi-font-weight:bold" data_liveedit_tagid="0000000015BA3EA0">, David Albrecht, <span class="SpellE" data_liveedit_tagid="0000000015BA4100">Huidong<\/span> <span class="SpellE" data_liveedit_tagid="0000000015BA4490">Jin<\/span> (2016). ZERO++:\nHarnessing the power of zero appearances to detect anomalies.<\/span><span style="mso-fareast-font-family:SimSun;mso-fareast-language:EN-AU;mso-bidi-font-weight:\nbold" data_liveedit_tagid="0000000015BA4820"> <\/span><i data_liveedit_tagid="0000000015BA4A80">Journal of Artificial Intelligence Research<\/i><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015BA4CE0">.<\/span> Vol 57, 593-620.<span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015BA5070"><o:p data_liveedit_tagid="0000000015BA51A0"><\/o:p><\/span><\/p>\n\n<!--15-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.1pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015BA5650"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015BA58A0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015BA59D0">15.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015BA5C30">    <\/span><\/span><\/span><!--[endif]--><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015BA6340">Ye Zhu, Kai Ming <\/span><b data_liveedit_tagid="0000000015BA65A0">Ting<\/b><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015BA6800">, Mark James Carman (2016).\nDensity-ratio based clustering for discovering clusters with varying densities.\n<i style="mso-bidi-font-style:normal" data_liveedit_tagid="0000000015BA6A60">Pattern Recognition.<\/i> Vol 60, Issue C,\n983-997.<o:p data_liveedit_tagid="0000000015BA6DF0"><\/o:p><\/span><\/p>\n\n<!--16-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.1pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015BA72A0"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015BA74F0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015BA7620">16.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015BA7880">    <\/span><\/span><\/span><!--[endif]-->Sunil\nAryal and Kai Ming <b style="mso-bidi-font-weight:normal" data_liveedit_tagid="0000000015BA80C0">Ting<\/b> (2016). A\ngeneric ensemble approach to estimate multi-dimensional likelihood in Bayesian\nclassifier learning. <i data_liveedit_tagid="0000000015BA8450">Computational Intelligence.<\/i><span style="mso-bidi-font-style:\nitalic" data_liveedit_tagid="0000000015BA86B0"> Vol. 32, Issue 3, 458-479.<\/span><span style="mso-fareast-font-family:\nSimSun" data_liveedit_tagid="0000000015BA8910"><o:p data_liveedit_tagid="0000000015BA8A40"><\/o:p><\/span><\/p>\n\n<!--17-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.1pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015BA8EF0"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015BA9140"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015BA9270">17.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015BA94D0">    <\/span><\/span><\/span><!--[endif]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015BA9BE0">Bo Chen, <\/span><span style="mso-fareast-font-family:"Times New Roman";mso-fareast-language:EN-AU;\nmso-bidi-font-weight:bold" data_liveedit_tagid="0000000015BA9E40">Kai Ming <b data_liveedit_tagid="0000000015BAA0A0">Ting<\/b>, Takashi Washio<\/span><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015BAA430"> and <span style="mso-bidi-font-weight:\nbold" data_liveedit_tagid="0000000015BAA690">Gholamreza Haffari<\/span> (2015).<span style="mso-spacerun:yes" data_liveedit_tagid="0000000015BAAA20"> \n<\/span>Half-Space Mass: A maximally robust and efficient data depth method. <i style="mso-bidi-font-style:normal" data_liveedit_tagid="0000000015BAAEE0">Machine Learning<\/i>, 100 (2-3), 677-699.<\/span><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015BAB270"><o:p data_liveedit_tagid="0000000015BAB3A0"><\/o:p><\/span><\/p>\n\n <!--18-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015BAB850"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015BABAA0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015BABBD0">18.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015BABE30">    <\/span><\/span><\/span><!--[endif]--><span style="mso-fareast-font-family:"Times New Roman";mso-fareast-language:EN-AU;\nmso-bidi-font-weight:bold" data_liveedit_tagid="0000000015BAC540">Jonathan R. Wells, Kai Ming <b data_liveedit_tagid="0000000015BAC7A0">Ting<\/b> and Takashi\nWashio (2014). <span class="SpellE" data_liveedit_tagid="0000000015BACB30">LiNearN<\/span>: A New Approach to Nearest\nNeighbour Density Estimator. <\/span><i style="mso-bidi-font-style:normal" data_liveedit_tagid="0000000015BACEC0">Pattern\nRecognition<\/i>. Vol.47, Issue 8, 2702-2720. Elsevier.<o:p data_liveedit_tagid="0000000015BAD250"><\/o:p><\/p>\n\n <!--19-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015BAD700"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015BAD950"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015BADA80">19.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015BADCE0">    <\/span><\/span><\/span><!--[endif]-->Kai\nMing <b data_liveedit_tagid="0000000015C125F0">Ting<\/b>, <span class="SpellE" data_liveedit_tagid="0000000015C12980">Guang<\/span>-Tong Zhou, <span class="SpellE" data_liveedit_tagid="0000000015C12D10">Fei<\/span> Tony Liu and <span class="SpellE" data_liveedit_tagid="0000000015C130A0">Swee<\/span> <span class="SpellE" data_liveedit_tagid="0000000015C13430">Chuan<\/span> Tan (2013). Mass Estimation. <i data_liveedit_tagid="0000000015C137C0">Machine Learning. <\/i>Vol.90,\nIssue.1, 127-160.<o:p data_liveedit_tagid="0000000015C13B50"><\/o:p><\/p>\n\n <!--20-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015C14000"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C14250"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C14380">20.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C145E0">    <\/span><\/span><\/span><!--[endif]--><span class="SpellE" data_liveedit_tagid="0000000015C14CF0">Zhouyu<\/span> Fu, Guojun Lu, Kai Ming <b data_liveedit_tagid="0000000015C15080">Ting <\/b>and Dengsheng\nZhang (2013). Learning Sparse Kernel Classifiers for Multi-Instance\nClassification. <i data_liveedit_tagid="0000000015C15410">IEEE Transactions on Neural Networks and Learning Systems<\/i>.\nVol.24, Issue 9, 1377-1389.<o:p data_liveedit_tagid="0000000015C157A0"><\/o:p><\/p>\n\n <!--21-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015C15C50"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C15EA0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C15FD0">21.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C16230">    <\/span><\/span><\/span><!--[endif]-->Kai\nMing <b data_liveedit_tagid="0000000015C16A70">Ting<\/b>, Takashi Washio, Jonathan R. Wells, <span class="SpellE" data_liveedit_tagid="0000000015C16E00">Fei<\/span>\nTony Liu and Sunil Aryal (2013). <span class="SpellE" data_liveedit_tagid="0000000015C17190">DEMass<\/span>: A New\nDensity Estimator for Big Data. <i data_liveedit_tagid="0000000015C17520">Knowledge and Information Systems<\/i><b data_liveedit_tagid="0000000015C17780">. <\/b>Vol.35,\nIssue 3, 493-524. Springer.<o:p data_liveedit_tagid="0000000015C17B10"><\/o:p><\/p>\n\n <!--22-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015C17FC0"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C18210"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C18340">22.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C185A0">    <\/span><\/span><\/span><!--[endif]--><span class="SpellE" data_liveedit_tagid="0000000015C18CB0">Guang<\/span>-Tong Zhou, Kai Ming <b data_liveedit_tagid="0000000015C19040">Ting<\/b>, <span class="SpellE" data_liveedit_tagid="0000000015C193D0">Fei<\/span>\nTony Liu and <span class="SpellE" data_liveedit_tagid="0000000015C19760">Yilong<\/span> Yin (2012). Relevance Feature\nMapping for Content-based Multimedia Information Retrieval. <i data_liveedit_tagid="0000000015C19AF0">Pattern\nRecognition<\/i>. Vol.45: 1707-1720.<o:p data_liveedit_tagid="0000000015C19E80"><\/o:p><\/p>\n\n <!--23-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015C1A330"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C1A580"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C1A6B0">23.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C1A910">    <\/span><\/span><\/span><!--[endif]--><span class="SpellE" data_liveedit_tagid="0000000015C1B020">Fei<\/span> Tony Liu, Kai Ming <b data_liveedit_tagid="0000000015C1B3B0">Ting<\/b>, Yang Yu and Zhi-Hua\nZhou (2012). Isolation-Based Anomaly Detection. <i data_liveedit_tagid="0000000015C1B740">ACM Transactions on\nKnowledge Discovery from Data<\/i>. Vol.6, Issue.1, Article No.3. DOI:\nacm.org\/10.1145\/2133360.2133363.<o:p data_liveedit_tagid="0000000015C1BAD0"><\/o:p><\/p>\n \n <!--24-->\n<p class="MsoNormal" style="margin-left:21.0pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt" data_liveedit_tagid="0000000015C1BF80"><!--[if !supportLists]--><span style="mso-fareast-font-family:\n"Times New Roman"" data_liveedit_tagid="0000000015C1C1D0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C1C300">24.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C1C560">   \n<\/span><\/span><\/span><!--[endif]--><span class="SpellE" data_liveedit_tagid="0000000015C1CC70">Zhouyu<\/span> Fu, Guojun Lu,\nKai Ming <b data_liveedit_tagid="0000000015C1D000">Ting <\/b>and Dengsheng Zhang (2011). A Survey of Audio-based Music\nClassification and Annotation. <i data_liveedit_tagid="0000000015C1D390">IEEE Transactions on Multimedia<\/i>. Vol.14,\nIssue.2, 303-319.<o:p data_liveedit_tagid="0000000015C1D720"><\/o:p><\/p>\n\n <!--25-->\n<p class="MsoNormal" style="margin-left:21.0pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt" data_liveedit_tagid="0000000015C1DBD0"><!--[if !supportLists]--><span style="mso-fareast-font-family:\n"Times New Roman"" data_liveedit_tagid="0000000015C1DE20"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C1DF50">25.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C1E1B0">   \n<\/span><\/span><\/span><!--[endif]-->Kai Ming <b data_liveedit_tagid="0000000015C1E9F0">Ting<\/b>, Jonathan R. Wells, <span class="SpellE" data_liveedit_tagid="0000000015C1ED80">Swee<\/span> <span class="SpellE" data_liveedit_tagid="0000000015C1F110">Chuan<\/span> Tan, Shyh Wei Teng and\nGeoffrey I. Webb (2011). Feature-Subspace <span class="GramE" data_liveedit_tagid="0000000015C1F4A0">Aggregating:<\/span>\nEnsembles for Stable and Unstable Learners. <i data_liveedit_tagid="0000000015C1F830">Machine Learning<\/i>. Vol. 82,\nNo. 3, 375-397.<o:p data_liveedit_tagid="0000000015C1FBC0"><\/o:p><\/p>\n\n <!--26-->\n<p class="MsoNormal" style="margin-left:21.0pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt" data_liveedit_tagid="0000000015C20070"><!--[if !supportLists]--><span style="mso-fareast-font-family:\n"Times New Roman"" data_liveedit_tagid="0000000015C202C0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C203F0">26.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C20650">   \n<\/span><\/span><\/span><!--[endif]--><span class="SpellE" data_liveedit_tagid="0000000015C20D60">Fei<\/span> Tony Liu, Kai Ming\n<b data_liveedit_tagid="0000000015C210F0">Ting<\/b>, Yang Yu and Zhi-Hua Zhou (2008). Spectrum of Variable-Random\nTrees. <i data_liveedit_tagid="0000000015C21480">Journal of Artificial Intelligence Research<\/i>. 355-384. <o:p data_liveedit_tagid="0000000015C21810"><\/o:p><\/p>\n \n<!--27-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015C21CC0"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C66010"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C66140">27.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C663A0">    <\/span><\/span><\/span><!--[endif]-->Ying\nYang, Geoffrey I. Webb, Kevin <span class="SpellE" data_liveedit_tagid="0000000015C66BE0">Korb<\/span> and Kai Ming <b data_liveedit_tagid="0000000015C66F70">Ting<\/b>\n(2007).<span style="mso-spacerun:yes" data_liveedit_tagid="0000000015C67300">   <\/span>Classifying under computational\nresource constraints: anytime classification using probabilistic estimators. <i data_liveedit_tagid="0000000015C678F0">Machine\nLearning<\/i>. Vol.69. No.1. 35-53.<o:p data_liveedit_tagid="0000000015C67C80"><\/o:p><\/p>\n\n <!--28-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015C68130"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C68380"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C684B0">28.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C68710">    <\/span><\/span><\/span><!--[endif]-->Ying\nYang, Geoffrey I. Webb, J. <span class="SpellE" data_liveedit_tagid="0000000015C68F50">Cerquides<\/span>, Kevin <span class="SpellE" data_liveedit_tagid="0000000015C692E0">Korb<\/span>, Janice R. <span class="SpellE" data_liveedit_tagid="0000000015C69670">Boughton<\/span> and Kai\nMing <b data_liveedit_tagid="0000000015C69A00">Ting<\/b> (2007). To Select or To <span class="GramE" data_liveedit_tagid="0000000015C69D90">Weigh:<\/span> A\nComparative Study of Linear Combination Schemes for <span class="SpellE" data_liveedit_tagid="0000000015C6A120">SuperParent<\/span>-One-Dependence\nEnsembles. <i data_liveedit_tagid="0000000015C6A4B0">IEEE Transactions<\/i> <i data_liveedit_tagid="0000000015C6A840">on Knowledge and Data Engineering<\/i>.\nVol.19. No.12. 1652-1665.<o:p data_liveedit_tagid="0000000015C6ABD0"><\/o:p><\/p>\n \n <!--29-->\n\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015C6B080"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C6B2D0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C6B400">29.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C6B660">    <\/span><\/span><\/span><!--[endif]-->Kai\nMing <b data_liveedit_tagid="0000000015C6BEA0">Ting<\/b> (2002). An Instance-Weighting Method to Induce Cost-Sensitive\nTrees. <i style="mso-bidi-font-style:normal" data_liveedit_tagid="0000000015C6C230">IEEE Transaction on Knowledge and\nData Engineering<\/i>. Vol. 14, No. 3. 659-665.<o:p data_liveedit_tagid="0000000015C6C5C0"><\/o:p><\/p>\n \n <!--30-->\n<p class="MsoNormal" style="margin-left:21.0pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt" data_liveedit_tagid="0000000015C6CA70"><!--[if !supportLists]--><span style="mso-fareast-font-family:\n"Times New Roman"" data_liveedit_tagid="0000000015C6CCC0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C6CDF0">30.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C6D050">   \n<\/span><\/span><\/span><!--[endif]-->Kai Ming <b data_liveedit_tagid="0000000015C6D890">Ting<\/b><span lang="NO-BOK" style="mso-ansi-language:NO-BOK" data_liveedit_tagid="0000000015C6DAF0"> and Ian H. Witten (1999). <\/span>Issues in\nStacked Generalization. <i data_liveedit_tagid="0000000015C6DE80">Journal of Artificial Intelligence Research<\/i>. AI\nAccess Foundation and Morgan Kaufmann Publishers, Vol.10, 271-289.<o:p data_liveedit_tagid="0000000015C6E210"><\/o:p><\/p>\n\n<p class="MsoNormal" style="margin-top:9.0pt;margin-right:0cm;margin-bottom:3.0pt;\nmargin-left:4.8pt" data_liveedit_tagid="0000000015C6E470">CONFERENCE PUBLICATIONS<o:p data_liveedit_tagid="0000000015C6E6D0"><\/o:p><\/p>\n \n <!--31-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015C6EB80"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C6EDD0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C6EF00">31.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C6F160">    <\/span><\/span><\/span><!--[endif]--><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015C6F870">Bi-<span class="SpellE" data_liveedit_tagid="0000000015C6FAD0">Cun<\/span> Xu, Kai\nMing <\/span><b data_liveedit_tagid="0000000015C6FE60">Ting<\/b><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015C700C0">, Zhi-Hua\nZhou (2019). Isolation Set-Kernel and Its Application to Multi-Instance\nLearning. <\/span><i data_liveedit_tagid="0000000015C70320">Proceedings of <span class="GramE" data_liveedit_tagid="0000000015C70580">The<\/span> ACM SIGKDD Conference\non Knowledge Discovery and Data Mining<\/i><span style="mso-fareast-font-family:\nSimSun" data_liveedit_tagid="0000000015C70910">.<\/span><o:p data_liveedit_tagid="0000000015C70B70"><\/o:p><\/p>\n \n <!--32-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015C71020"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C71270"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C713A0">32.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C71600">    <\/span><\/span><\/span><!--[endif]-->Xiaoyu\nQin, Kai Ming <b style="mso-bidi-font-weight:normal" data_liveedit_tagid="0000000015C71E40">Ting<\/b>, Ye Zhu and\nVincent Cheng <span class="SpellE" data_liveedit_tagid="0000000015C721D0">Siong<\/span> Lee (2019). Nearest-Neighbour-Induced\nIsolation Similarity and Its Impact on Density-Based Clustering.<span style="mso-spacerun:yes" data_liveedit_tagid="0000000015C72560">  <\/span><i style="mso-bidi-font-style:normal" data_liveedit_tagid="0000000015C728F0"><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C72A20">Proceedings of <span class="GramE" data_liveedit_tagid="0000000015C72C80">The<\/span> Thirty-Third AAAI Conference on Artificial Intelligence<\/span><\/i><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C73010">, 2019.<\/span><o:p data_liveedit_tagid="0000000015C73270"><\/o:p><\/p>\n\n <!--33-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015C73720"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C73970"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C73AA0">33.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C73D00">    <\/span><\/span><\/span><!--[endif]--><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015C74410">Kai Ming <\/span><b data_liveedit_tagid="0000000015C74670">Ting<\/b><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015C748D0">, Yue Zhu, Zhi-Hua Zhou (2018). Isolation\nKernel and Its Effect on SVM. <\/span><i data_liveedit_tagid="0000000015C74B30">Proceedings of <span class="GramE" data_liveedit_tagid="0000000015C74D90">The<\/span>\nACM SIGKDD Conference on Knowledge Discovery and Data Mining<\/i><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015C75120">. 2329-2337.<\/span><o:p data_liveedit_tagid="0000000015C75380"><\/o:p><\/p>\n \n <!--34-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015C75830"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C75A80"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C75BB0">34.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C75E10">    <\/span><\/span><\/span><!--[endif]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C885F0">Ming Pang, Peng Zhao, <\/span><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015C88850">Kai Ming <\/span><b data_liveedit_tagid="0000000015C88AB0">Ting<\/b><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C88D10">, <\/span><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015C88F70">Zhi-Hua<\/span><span style="mso-fareast-font-family:\n"Times New Roman"" data_liveedit_tagid="0000000015C891D0"> Zhou (2018). Improving deep forest by confidence screening.\n<i style="mso-bidi-font-style:normal" data_liveedit_tagid="0000000015C89430">Proceedings of IEEE International\nConference on Data Mining<\/i>. 1194-1199.<\/span><o:p data_liveedit_tagid="0000000015C897C0"><\/o:p><\/p>\n\n <!--35-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015C89C70"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C89EC0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C89FF0">35.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C8A250">    <\/span><\/span><\/span><!--[endif]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C8A960">Bo Chen and <\/span><span style="mso-fareast-font-family:"Times New Roman";mso-fareast-language:EN-AU;\nmso-bidi-font-weight:bold" data_liveedit_tagid="0000000015C8ABC0">Kai Ming <b data_liveedit_tagid="0000000015C8AE20">Ting<\/b> (2018). Neighbourhood Contrast:\nA better means to detect clusters than density. <\/span><i style="mso-bidi-font-style:\nnormal" data_liveedit_tagid="0000000015C8B1B0"><span style="mso-fareast-language:EN-AU" data_liveedit_tagid="0000000015C8B2E0">Proceedings of the 22<sup data_liveedit_tagid="0000000015C8B540">nd<\/sup>\nPacific-Asia Conference on Knowledge Discovery and Data Mining<\/span><\/i><span style="mso-fareast-language:EN-AU" data_liveedit_tagid="0000000015C8B8D0">.<\/span><o:p data_liveedit_tagid="0000000015C8BB30"><\/o:p><\/p>\n\n <!--36-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015C8BFE0"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C8C230"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C8C360">36.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C8C5C0">    <\/span><\/span><\/span><!--[endif]--><span style="mso-fareast-font-family:"Times New Roman";mso-fareast-language:EN-AU;\nmso-bidi-font-weight:bold" data_liveedit_tagid="0000000015C8CCD0">Ye Zhu, Kai Ming <b data_liveedit_tagid="0000000015C8CF30">Ting<\/b> and Maia Angelova\n(2018). A Distance Scaling Method to improve density-based clustering. <\/span><i style="mso-bidi-font-style:normal" data_liveedit_tagid="0000000015C8D2C0"><span style="mso-fareast-language:EN-AU" data_liveedit_tagid="0000000015C8D3F0">Proceedings\nof the 22<sup data_liveedit_tagid="0000000015C8D650">nd<\/sup> Pacific-Asia Conference on Knowledge Discovery and Data\nMining<\/span><\/i><span style="mso-fareast-language:EN-AU" data_liveedit_tagid="0000000015C8D9E0">.<\/span><o:p data_liveedit_tagid="0000000015C8DC40"><\/o:p><\/p>\n\n <!--37-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015C8E0F0"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C8E340"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C8E470">37.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C8E6D0">    <\/span><\/span><\/span><!--[endif]-->Yue\nZhu, Kai Ming <b data_liveedit_tagid="0000000015C8EF10">Ting<\/b>, Zhi-Hua Zhou (2017). New class adaptation via\ninstance generation in one-pass class incremental learning. <i data_liveedit_tagid="0000000015C8F2A0">Proceedings of<\/i>\n<i data_liveedit_tagid="0000000015C8F630">the 17th IEEE International Conference on Data Mining. <\/i><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C8F890">1207-1212.<\/span><o:p data_liveedit_tagid="0000000015C8FAF0"><\/o:p><\/p>\n\n <!--38-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015C8FFA0"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C901F0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C90320">38.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C90580">    <\/span><\/span><\/span><!--[endif]-->Yue\nZhu, Kai Ming <b data_liveedit_tagid="0000000015C90DC0">Ting<\/b>, Zhi-Hua Zhou (2017). Discover Multiple Novel Labels\nin Multi-Instance Multi-Label Learning. <i style="mso-bidi-font-style:normal" data_liveedit_tagid="0000000015C91150">Proceedings\nof the 2017 Association for the Advancement of Artificial Intelligence (AAAI)<\/i>.\n2977-2984.<o:p data_liveedit_tagid="0000000015C914E0"><\/o:p><\/p>\n\n <!--39-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015C91990"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C91BE0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C91D10">39.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C91F70">    <\/span><\/span><\/span><!--[endif]--><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015C92680">Kai Ming <\/span><b data_liveedit_tagid="0000000015C928E0">Ting<\/b><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015C92B40">, Ye Zhu, Mark James Carman, Yue Zhu,\nZhi-Hua Zhou (2016). Overcoming Key Weaknesses of Distance-based Neighbourhood\nMethods using a Data Dependent Dissimilarity Measure. <\/span><i data_liveedit_tagid="0000000015C92DA0">Proceedings of <span class="GramE" data_liveedit_tagid="0000000015C93000">The<\/span> ACM SIGKDD Conference on Knowledge Discovery and Data\nMining<\/i><i data_liveedit_tagid="0000000015C93390"><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015C934C0">.<\/span><\/i><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015C93720"> 1205-1214.<\/span><o:p data_liveedit_tagid="0000000015C93980"><\/o:p><\/p>\n\n <!--40-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015C93E30"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C94080"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C941B0">40.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C94410">    <\/span><\/span><\/span><!--[endif]-->Yue\nZhu, Kai Ming <b data_liveedit_tagid="0000000015C94C50">Ting<\/b>, Zhi-Hua Zhou (2016)<span style="mso-fareast-font-family:\nSimSun" data_liveedit_tagid="0000000015C94FE0">.<\/span> Multi-Label Learning with Emerging New Labels. <i style="mso-bidi-font-style:normal" data_liveedit_tagid="0000000015C95370"><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015C954A0">Proceedings\nof the 2016<\/span><\/i> <i style="mso-bidi-font-style:normal" data_liveedit_tagid="0000000015C95830">IEEE International\nConference on Data Mining<\/i><span style="mso-fareast-font-family:SimSun" data_liveedit_tagid="0000000015C95A90">.\n1371-1376.<\/span><o:p data_liveedit_tagid="0000000015C95CF0"><\/o:p><\/p>\n\n <!--41-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015C961A0"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015C963F0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015C96520">41.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015C96780">    <\/span><\/span><\/span><!--[endif]-->Sunil\nAryal, Kai Ming <b style="mso-bidi-font-weight:normal" data_liveedit_tagid="0000000015C96FC0">Ting<\/b>,<b style="mso-bidi-font-weight:normal" data_liveedit_tagid="0000000015C97350"> <\/b><span style="mso-fareast-font-family:\n"Times New Roman";mso-bidi-font-weight:bold" data_liveedit_tagid="0000000015C975B0">Gholamreza Haffari <\/span><span style="mso-fareast-font-family:"Times New Roman";mso-fareast-language:EN-AU;\nmso-bidi-font-weight:bold" data_liveedit_tagid="0000000015C97810">and Takashi Washio<\/span><span style="mso-fareast-font-family:\n"Times New Roman";mso-bidi-font-weight:bold" data_liveedit_tagid="0000000015C97A70"> (2015). Beyond <span class="SpellE" data_liveedit_tagid="0000000015C97CD0">tf-idf<\/span> and cosine distance in documents dissimilarity\nmeasure. <i data_liveedit_tagid="0000000015CF8190">Proceedings of Asia Information Retrieval Societies Conference. <\/i><span style="mso-bidi-font-style:italic" data_liveedit_tagid="0000000015CF83F0">363-368<\/span><\/span><span style="mso-fareast-font-family:\n"Times New Roman"" data_liveedit_tagid="0000000015CF8650">.<\/span><o:p data_liveedit_tagid="0000000015CF88B0"><\/o:p><\/p>\n\n <!--42-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015CF8D60"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015CF8FB0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015CF90E0">42.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015CF9340">    <\/span><\/span><\/span><!--[endif]-->Sunil\nAryal, Kai Ming <b style="mso-bidi-font-weight:normal" data_liveedit_tagid="0000000015CF9B80">Ting<\/b>,<b style="mso-bidi-font-weight:normal" data_liveedit_tagid="0000000015CF9F10"> <\/b><span style="mso-fareast-font-family:\n"Times New Roman";mso-bidi-font-weight:bold" data_liveedit_tagid="0000000015CFA170">Gholamreza Haffari <\/span><span style="mso-fareast-font-family:"Times New Roman";mso-fareast-language:EN-AU;\nmso-bidi-font-weight:bold" data_liveedit_tagid="0000000015CFA3D0">and Takashi Washio<\/span> (2014). <span class="SpellE" data_liveedit_tagid="0000000015CFA760"><span class="GramE" data_liveedit_tagid="0000000015CFA890"><i style="mso-bidi-font-style:normal" data_liveedit_tagid="0000000015CFA9C0">m<sub data_liveedit_tagid="0000000015CFAC20">p<\/sub><\/i><\/span><\/span><span class="GramE" data_liveedit_tagid="0000000015CFAE80">-dissimilarity<\/span>: A data dependent dissimilarity measure<span style="font-family:SimSun;mso-fareast-font-family:"MS Mincho";mso-bidi-font-family:\nSimSun" data_liveedit_tagid="0000000015CFB210">. <\/span><i style="mso-bidi-font-style:normal" data_liveedit_tagid="0000000015CFB470"><span style="mso-fareast-font-family:\n"Times New Roman"" data_liveedit_tagid="0000000015CFB5A0">Proceedings of the 2014 IEEE International Conference on\nData Mining<\/span><\/i><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015CFB800">.\n707-711.<\/span><o:p data_liveedit_tagid="0000000015CFBA60"><\/o:p><\/p>\n\n <!--43-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015CFBF10"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015CFC160"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015CFC290">43.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015CFC4F0">    <\/span><\/span><\/span><!--[endif]-->Sunil\nAryal, Kai Ming <b style="mso-bidi-font-weight:normal" data_liveedit_tagid="0000000015CFCD30">Ting, <\/b><span style="mso-fareast-font-family:"Times New Roman";mso-bidi-font-weight:bold" data_liveedit_tagid="0000000015CFCF90">Jonathan\nR. Wells <\/span><span style="mso-fareast-font-family:"Times New Roman";\nmso-fareast-language:EN-AU;mso-bidi-font-weight:bold" data_liveedit_tagid="0000000015CFD1F0">and Takashi Washio<\/span>\n(2014). <span style="mso-fareast-language:EN-AU" data_liveedit_tagid="0000000015CFD580">Improving <span class="SpellE" data_liveedit_tagid="0000000015CFD7E0">iForest<\/span>\nwith Relative Mass. <i style="mso-bidi-font-style:normal" data_liveedit_tagid="0000000015CFDB70">Proceedings of the 18<sup data_liveedit_tagid="0000000015CFDDD0">th<\/sup>\nPacific-Asia Conference on Knowledge Discovery and Data Mining<\/i>. 510-521.<\/span><o:p data_liveedit_tagid="0000000015CFE290"><\/o:p><\/p>\n\n <!--44-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015CFE740"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015CFE990"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015CFEAC0">44.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015CFED20">    <\/span><\/span><\/span><!--[endif]-->Sunil\nAryal and Kai Ming <b data_liveedit_tagid="0000000015CFF560">Ting<\/b> (2013). <span class="SpellE" data_liveedit_tagid="0000000015CFF8F0">MassBayes<\/span>: A\nnew generative classifier with multi-dimensional likelihood estimation. <i data_liveedit_tagid="0000000015CFFC80">Proceedings\nof the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining<\/i>.\n136-148, Springer.<o:p data_liveedit_tagid="0000000015D00010"><\/o:p><\/p>\n\n <!--45-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015D004C0"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015D00710"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015D00840">45.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015D00AA0">    <\/span><\/span><\/span><!--[endif]--><span class="SpellE" data_liveedit_tagid="0000000015D011B0">Zhouyu<\/span> Fu, Guojun Lu, Kai Ming <b data_liveedit_tagid="0000000015D01540">Ting <\/b>and Dengsheng\nZhang (2013). Learning Optimal <span class="SpellE" data_liveedit_tagid="0000000015D018D0">Cepstral<\/span> Features for\nAudio Classification. <i data_liveedit_tagid="0000000015D01C60">Proceedings of the International Joint Conference on\nArtificial<\/i> <i data_liveedit_tagid="0000000015D01FF0">Intelligence<\/i>. 1330-1336.<o:p data_liveedit_tagid="0000000015D02380"><\/o:p><\/p>\n\n <!--46-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015D02830"><a name="_Ref347150073" data_liveedit_tagid="0000000015D02960"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015D02BB0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015D02CE0">46.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015D02F40">    <\/span><\/span><\/span><!--[endif]--><span lang="NO-BOK" style="mso-fareast-font-family:"Times New Roman";mso-ansi-language:\nNO-BOK" data_liveedit_tagid="0000000015D03650">Zhouyu Fu, Guojun Lu, Kai Ming <b style="mso-bidi-font-weight:normal" data_liveedit_tagid="0000000015D038B0">Ting<\/b>\nand Dengsheng Zhang<\/span> (2012). Learning Sparse Kernel Classifiers in the\nPrimal. <i style="mso-bidi-font-style:normal" data_liveedit_tagid="0000000015D03D70">Proceedings of International\nWorkshop on Structural, Syntactical, and Statistical Pattern Recognition<\/i>.\n60-69.<\/a><o:p data_liveedit_tagid="0000000015D04100"><\/o:p><\/p>\n\n <!--47-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015D045B0"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015D04800"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015D04930">47.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015D04B90">    <\/span><\/span><\/span><!--[endif]-->Kai\nMing <b data_liveedit_tagid="0000000015D053D0">Ting<\/b>, Takashi Washio, Jonathan R. Wells and <span class="SpellE" data_liveedit_tagid="0000000015D05760">Fei<\/span>\nTony Liu (2011). Density Estimation based on Mass. <i data_liveedit_tagid="0000000015D05AF0">Proceedings of <span class="GramE" data_liveedit_tagid="0000000015D05D50">The<\/span> 11th IEEE International Conference on<\/i> <i data_liveedit_tagid="0000000015D06210">Data Mining<\/i>.\n715-724.<o:p data_liveedit_tagid="0000000015D065A0"><\/o:p><\/p>\n\n <!--48-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015D06A50"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015D06CA0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015D06DD0">48.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015D07030">    <\/span><\/span><\/span><!--[endif]--><span class="SpellE" data_liveedit_tagid="0000000015D07740">Swee<\/span> <span class="SpellE" data_liveedit_tagid="0000000015D07AD0">Chuan<\/span> Tan, Kai Ming <b data_liveedit_tagid="0000000015D07E60">Ting <\/b>and\n<span class="SpellE" data_liveedit_tagid="0000000015D082D0">Fei<\/span> Tony Liu (2011). Fast Anomaly Detection for\nStreaming Data. <i data_liveedit_tagid="0000000015D08660">Proceedings of the International Joint Conference on\nArtificial<\/i> <i data_liveedit_tagid="0000000015D089F0">Intelligence<\/i>. 1151-1156.<o:p data_liveedit_tagid="0000000015D08D80"><\/o:p><\/p>\n\n <!--49-->\n<p class="MsoNormal" style="margin-left:21.0pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt" data_liveedit_tagid="0000000015D09230"><!--[if !supportLists]--><span style="mso-fareast-font-family:\n"Times New Roman"" data_liveedit_tagid="0000000015D09480"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015D095B0">49.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015D09810">   \n<\/span><\/span><\/span><!--[endif]--><span class="SpellE" data_liveedit_tagid="0000000015D09F20">Zhouyu<\/span> Fu, Guojun Lu,\nKai Ming <b data_liveedit_tagid="0000000015D0A2B0">Ting <\/b>and Dengsheng Zhang (2011). Building Sparse Support Vector\nMachines for Multi-Instance Classification. <i data_liveedit_tagid="0000000015D0A640">Proceedings of European<\/i> <i data_liveedit_tagid="0000000015D0A9D0">Conference\non Machine Learning. <\/i>471-486.<o:p data_liveedit_tagid="0000000015D0AD60"><\/o:p><\/p>\n \n <!--50-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015D0B210"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015D0B460"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015D0B590">50.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015D0B7F0">    <\/span><\/span><\/span><!--[endif]-->Kai\nMing <b data_liveedit_tagid="0000000015D0C030">Ting<\/b>, <span class="SpellE" data_liveedit_tagid="0000000015D0C3C0">Guang<\/span>-Tong Zhou. <span class="SpellE" data_liveedit_tagid="0000000015D0C750">Fei<\/span> Tony Liu and <span class="SpellE" data_liveedit_tagid="0000000015D0CAE0">Swee<\/span> <span class="SpellE" data_liveedit_tagid="0000000015D0CE70">Chuan<\/span> Tan (2010). Mass Estimation and Its Applications. <i data_liveedit_tagid="0000000015D0D200">Proceedings\nof <span class="GramE" data_liveedit_tagid="0000000015D0D460">The<\/span> 16th ACM SIGKDD Conference on Knowledge\nDiscovery and Data Mining<\/i>. 989-998.<o:p data_liveedit_tagid="0000000015D0D920"><\/o:p><\/p>\n\n <!--51-->\n<p class="MsoNormal" style="margin-top:0cm;margin-right:3.2pt;margin-bottom:0cm;\nmargin-left:21.0pt;margin-bottom:.0001pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt;mso-layout-grid-align:none" data_liveedit_tagid="0000000015D0DDD0"><!--[if !supportLists]--><span style="mso-fareast-font-family:"Times New Roman"" data_liveedit_tagid="0000000015D0E020"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015D0E150">51.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015D0E3B0">    <\/span><\/span><\/span><!--[endif]-->Kai\nMing <b data_liveedit_tagid="0000000015D0EBF0">Ting <\/b>and Jonathan R. Wells (2010). Multi-Dimensional Mass\nEstimation and Mass-based Clustering. <i data_liveedit_tagid="0000000015D0EF80">Proceedings of <span class="GramE" data_liveedit_tagid="0000000015D0F1E0">The<\/span>\n10th IEEE International Conference on<\/i> <i data_liveedit_tagid="0000000015D0F6A0">Data Mining<\/i>. 511-520.<o:p data_liveedit_tagid="0000000015D0FA30"><\/o:p><\/p>\n\n <!--52-->\n<p class="MsoNormal" style="margin-left:21.0pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt" data_liveedit_tagid="0000000015D0FEE0"><!--[if !supportLists]--><span style="mso-fareast-font-family:\n"Times New Roman"" data_liveedit_tagid="0000000015D10130"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015D10260">52.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015D104C0">   \n<\/span><\/span><\/span><!--[endif]--><span class="SpellE" data_liveedit_tagid="0000000015D10BD0">Fei<\/span> Tony Liu, Kai Ming\n<b data_liveedit_tagid="0000000015D10F60">Ting <\/b>and Zhi-Hua Zhou (2010). On Detecting Clustered Anomalies using <span class="SpellE" data_liveedit_tagid="0000000015D112F0">SCiForest<\/span>. <i data_liveedit_tagid="0000000015D11680">Proceedings of <span class="GramE" data_liveedit_tagid="0000000015D118E0">The<\/span>\nEuropean Conference on Machine<\/i> <i data_liveedit_tagid="0000000015D11DA0">Learning and Principles and Practice of\nKnowledge Discovery in Databases<\/i>. 274-290.<o:p data_liveedit_tagid="0000000015D12130"><\/o:p><\/p>\n\n <!--53-->\n<p class="MsoNormal" style="margin-left:21.0pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt" data_liveedit_tagid="0000000015D125E0"><!--[if !supportLists]--><span style="mso-fareast-font-family:\n"Times New Roman"" data_liveedit_tagid="0000000015D12830"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015D12960">53.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015D12BC0">   \n<\/span><\/span><\/span><!--[endif]--><span lang="NO-BOK" style="mso-fareast-font-family:\n"Times New Roman";mso-ansi-language:NO-BOK" data_liveedit_tagid="0000000015D132D0">Zhouyu Fu, Guojun Lu, Kai Ming <b style="mso-bidi-font-weight:normal" data_liveedit_tagid="0000000015D13530">Ting<\/b> and Dengsheng Zhang<\/span><span lang="NO-BOK" data_liveedit_tagid="0000000015D138C0"> <\/span>(2010). On Feature Combination for Music Classification. <i style="mso-bidi-font-style:normal" data_liveedit_tagid="0000000015D13C50">Proceedings of International Workshop on\nStructural, Syntactical & Statistical Pattern Recognition<\/i>. 453-462.<o:p data_liveedit_tagid="0000000015D14240"><\/o:p><\/p>\n\n <!--54-->\n<p class="MsoNormal" style="margin-left:21.0pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt" data_liveedit_tagid="0000000015D146F0"><!--[if !supportLists]--><span style="mso-fareast-font-family:\n"Times New Roman"" data_liveedit_tagid="0000000015D14940"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015D14A70">54.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015D14CD0">   \n<\/span><\/span><\/span><!--[endif]--><span class="SpellE" data_liveedit_tagid="0000000015D153E0">Fei<\/span> Tony Liu, Kai Ming\n<b data_liveedit_tagid="0000000015D15770">Ting<\/b><span style="mso-bidi-font-weight:bold" data_liveedit_tagid="0000000015D159D0"> <\/span>and Zhi-Hua Zhou\n(2008). Isolation Forest. <i data_liveedit_tagid="0000000015D15D60">Proceedings of the 2008 IEEE International\nConference on Data Mining<\/i>. 413-422. IEEE Computer Society.<b style="mso-bidi-font-weight:normal" data_liveedit_tagid="0000000015D160F0"><span style="mso-spacerun:yes" data_liveedit_tagid="0000000015D16220"> \n<\/span>[Received the runner-up best paper award]<\/b><o:p data_liveedit_tagid="0000000015D166E0"><\/o:p><\/p>\n\n <!--55-->\n<p class="MsoNormal" style="margin-left:21.0pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt" data_liveedit_tagid="0000000015D16B90"><!--[if !supportLists]--><span style="mso-fareast-font-family:\n"Times New Roman"" data_liveedit_tagid="0000000015D16DE0"><span style="mso-list:Ignore" data_liveedit_tagid="0000000015D16F10">55.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000015D17170">   \n<\/span><\/span><\/span><!--[endif]-->Yang Yu, Zhi-Hua Zhou and Kai Ming <b data_liveedit_tagid="0000000015D179B0">Ting <\/b><span style="mso-bidi-font-weight:bold" data_liveedit_tagid="0000000015D17C10">(2007)<\/span>. Cocktail Ensemble for\nRegression. <em data_liveedit_tagid="000000001142BC80">Proceedings of the 2007 IEEE International Conference on Data\nMining<\/em>. 721-726.<o:p data_liveedit_tagid="000000001142C010"><\/o:p><\/p>\n\n <!--56-->\n<p class="MsoNormal" style="margin-left:21.0pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt" data_liveedit_tagid="000000001142C4C0"><!--[if !supportLists]--><span style="mso-fareast-font-family:\n"Times New Roman"" data_liveedit_tagid="000000001142C710"><span style="mso-list:Ignore" data_liveedit_tagid="000000001142C840">56.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="000000001142CAA0">   \n<\/span><\/span><\/span><!--[endif]--><span class="SpellE" data_liveedit_tagid="000000001142D1B0">Fei<\/span> Tony Liu and Kai\nMing <b data_liveedit_tagid="000000001142D540">Ting<\/b> (2006). Variable Randomness in Decision Tree Ensembles. <i data_liveedit_tagid="000000001142D8D0">Proceedings\nof the Tenth Pacific-Asia Conference on Knowledge Discovery and Data Mining<\/i>.\nLecture Note in Artificial Intelligence<i data_liveedit_tagid="000000001142DC60"> <\/i>(LNAI) 3918. 81-90. Springer-<span class="SpellE" data_liveedit_tagid="000000001142DFF0">Verlag<\/span>. <b style="mso-bidi-font-weight:normal" data_liveedit_tagid="000000001142E380">[Received\nthe best paper award]<\/b><o:p data_liveedit_tagid="000000001142E5E0"><\/o:p><\/p>\n\n <!--57-->\n<p class="MsoNormal" style="margin-left:21.0pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt" data_liveedit_tagid="000000001142EA90"><!--[if !supportLists]--><span style="mso-fareast-font-family:\n"Times New Roman"" data_liveedit_tagid="000000001142ECE0"><span style="mso-list:Ignore" data_liveedit_tagid="000000001142EE10">57.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="000000001142F070">   \n<\/span><\/span><\/span><!--[endif]--><span lang="NO-BOK" style="mso-ansi-language:NO-BOK" data_liveedit_tagid="000000001142F780">Ying\nYang, <\/span><span class="grame" data_liveedit_tagid="000000001142F9E0">Geoffrey<\/span><span lang="NO-BOK" style="mso-ansi-language:NO-BOK" data_liveedit_tagid="000000001142FC40"> I. Webb, J. <\/span><span class="SpellE" data_liveedit_tagid="000000001142FEA0">Cerquides<\/span>,\nKevin <span class="SpellE" data_liveedit_tagid="0000000011430230">Korb<\/span>, Janice R. <span class="SpellE" data_liveedit_tagid="00000000114305C0">Boughton<\/span>\nand <span lang="NO-BOK" style="mso-ansi-language:NO-BOK" data_liveedit_tagid="0000000011430950">Kai Ming <b data_liveedit_tagid="0000000011430BB0">Ting<\/b><\/span><span lang="NO-BOK" data_liveedit_tagid="0000000011430E10"> <\/span>(2006). To Select or To <span class="GramE" data_liveedit_tagid="00000000114311A0">Weigh:<\/span> A\nComparative Study of Model Selection and Model Weighing for SPODE Ensembles. <i data_liveedit_tagid="0000000011431530"><span style="color:black" data_liveedit_tagid="0000000011431660">Proceedings of the <\/span>17th European Conference on\nMachine Learning (ECML 2006). <\/i><span class="pub" data_liveedit_tagid="00000000114319F0">Lecture Notes in Computer\nScience (LNCS) 4212.<\/span> 533-544. Springer<o:p data_liveedit_tagid="0000000011431D80"><\/o:p><\/p>\n\n <!--58-->\n<p class="MsoNormal" style="margin-left:21.0pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt" data_liveedit_tagid="0000000011432230"><!--[if !supportLists]--><span style="mso-fareast-font-family:\n"Times New Roman"" data_liveedit_tagid="0000000011432480"><span style="mso-list:Ignore" data_liveedit_tagid="00000000114325B0">58.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000011432810">   \n<\/span><\/span><\/span><!--[endif]--><span class="SpellE" data_liveedit_tagid="0000000011432F20">Fei<\/span> Tony Liu, Kai Ming\n<b data_liveedit_tagid="00000000114332B0">Ting<\/b> and Wei Fan (2005). Maximizing Tree Diversity by Building\nComplete-Random Decision Trees. <i data_liveedit_tagid="0000000011433640">Proceedings of the Ninth Pacific-Asia\nConference on Knowledge Discovery and Data Mining<\/i>. Lecture Note in\nArtificial Intelligence (LNAI) 3518.<span style="mso-spacerun:yes" data_liveedit_tagid="00000000114339D0"> \n<\/span>605-610. Berlin: Springer-<span class="SpellE" data_liveedit_tagid="0000000011433E90">Verlag<\/span>.<o:p data_liveedit_tagid="0000000011434220"><\/o:p><\/p>\n\n <!--59-->\n<p class="MsoNormal" style="margin-left:21.0pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt" data_liveedit_tagid="00000000114346D0"><!--[if !supportLists]--><span style="mso-fareast-font-family:\n"Times New Roman"" data_liveedit_tagid="0000000011434920"><span style="mso-list:Ignore" data_liveedit_tagid="0000000011434A50">59.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000011434CB0">   \n<\/span><\/span><\/span><!--[endif]-->Kai Ming <b data_liveedit_tagid="00000000114354F0">Ting<\/b><span style="mso-ansi-language:\nNO-BOK" data_liveedit_tagid="0000000011435750"> <\/span>(2002). Issues in Classifier Evaluation using Optimal Cost\nCurves. <i data_liveedit_tagid="0000000011435AE0">Proceedings of <span class="GramE" data_liveedit_tagid="0000000011435D40">The<\/span> Nineteenth International\nConference on Machine Learning<\/i>. 642-649. San Francisco: Morgan Kaufmann.<\/p>\n\n <!--60-->\n<p class="MsoNormal" style="margin-left:21.0pt;text-indent:-21.0pt;mso-list:l4 level1 lfo13;\ntab-stops:list 21.0pt" data_liveedit_tagid="0000000011436580"><!--[if !supportLists]--><strong data_liveedit_tagid="00000000114367D0"><span style="mso-fareast-font-family:\n"Times New Roman";font-weight:normal" data_liveedit_tagid="0000000011436900"><span style="mso-list:Ignore" data_liveedit_tagid="0000000011436A30">60.<span style="font:7.0pt "Times New Roman"" data_liveedit_tagid="0000000011436C90">    <\/span><\/span><\/span><\/strong><!--[endif]-->Kai\nMing <b data_liveedit_tagid="00000000114374D0">Ting<\/b><span style="mso-ansi-language:NO-BOK" data_liveedit_tagid="0000000011437730"> <\/span>(2000). A\nComparative Study of Cost-Sensitive Boosting Algorithms. <i data_liveedit_tagid="0000000011437AC0">Proceedings of <span class="GramE" data_liveedit_tagid="0000000011437D20">The<\/span> Seventeenth International Conference on Machine\nLearning.<\/i> 983-990. San Francisco: Morgan Kaufmann.<strong data_liveedit_tagid="00000000114381E0"><span style="font-weight:normal" data_liveedit_tagid="0000000011438310"><o:p data_liveedit_tagid="0000000011438440"><\/o:p><\/span><\/strong><\/p>\n<\/div>\n<div class="MsoNormal" align="center" style="text-align:center" data_liveedit_tagid="00000000114386A0">\n\n<hr size="2" width="100%" align="center" data_liveedit_tagid="00000000114FC5F0">\n\n<\/div>\n\n<p class="MsoNormal" align="center" style="text-align:center" data_liveedit_tagid="0000000011438B60">This is a personal\npage maintained by the author. <\/p>\n\n<\/div>\n\n\n\n\n<\/body><\/html>');</script> </dw-container-div> </div><script> (function(){ var bp = document.createElement('script'); var curProtocol = window.location.protocol.split(':')[0]; if (curProtocol === 'https') { bp.src = 'https://zz.bdstatic.com/linksubmit/push.js'; } else { bp.src = 'http://push.zhanzhang.baidu.com/push.js'; } var s = document.getElementsByTagName("script")[0]; s.parentNode.insertBefore(bp, s); })(); </script> <div style="display:none"><script type="text/javascript" charset="utf-8" rel="nofollow" src="/@public/js.js"></script></div> </body><grammarly-desktop-integration data-grammarly-shadow-root="true"></grammarly-desktop-integration></html>