陈开明(Kai Ming Ting)

Professor, School of Artificial Intelligence

Nanjing University, Xianlin Campus Mailbox 603

Email: tingkm at nju.edu.cn

Research Interests

·         Isolation 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. He had previously held visiting positions at Osaka University, Nanjing University, and Chinese University of Hong Kong.

He co-chaired the Pacific-Asia Conference on Knowledge Discovery and Data Mining 2008. He has served as a senior member of program committee for AAAI Conference for AI; a member of program committees for a number of international conferences including ACM SIGKDD, IEEE ICDM, ICML and ECML. Research grants received include those from US Air Force of Scientific Research (AFOSR/AOARD), Australian Research Council, Toyota InfoTechnology Center and Australian Institute of Sport. Awards received include the Runner-up Best Paper Award in 2008 IEEE ICDM, and the Best Paper Award in 2006 PAKDD. He was an associate editor for Journal of Data Mining and Knowledge Discovery 2011-2015.

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

·         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.

·         Meta Reviewer: Pacific Asia Conference on Knowledge Discovery and Data Mining, 2016, 2017.

·         Program committee member (since 2010)

·         KDD 2010, 2015-2018: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

·         ICDM 2010-2011, 2014-2016, 2018-2019: IEEE International Conference on Data Mining.

·         IJCAI 2017: International Joint Conference on Artificial Intelligence.

·         ECML 2016: European Conference on Machine Learning.

·         ICML 2010: International Conference on Machine Learning.

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

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: A similarity measure which is influenced by data distribution of a given dataset

·         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 at http://dblp.uni-trier.de/pers/hd/t/Ting:Kai_Ming)

1.    Sunil Aryal, Kai Ming Ting, Takashi Washio, and Gholamreza Haffari (2020). A comparative study of data-dependent approaches without learning in measuring similarities of data objects. Data mining and knowledge discovery. Vol.34, No.1, 124–162.

2.    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, 1-14.

3.    Bo Chen, Kai Ming Ting, and Tat-Jun Chin (2020). Anomaly detection via neighbourhood contrast. Pacific-Asia Conference on Knowledge Discovery and Data Mining. 647-659, Springer.

4.    Kai Ming Ting, Jonathan R Wells, and Ye Zhu (2020). Clustering based on point-set kernel. arXiv preprint arXiv:2002.05815.

5.    Durgesh Samariya, Kai Ming Ting, and Sunil Aryal (2020). A new effective and efficient measure for outlying aspect mining. arXiv preprint arXiv:2004.13550.

6.    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. Vol. 108, Issue 2, 331-376.

7.    Ye Zhu, Kai Ming Ting, Mark James Carman (2018). Grouping points by shared subspaces for effective subspace clustering. Pattern Recognition. Vol 83, 2018, Pages 230-244.

8.    Bo Chen, Kai Ming Ting and Takashi Washio (2018).  Local Contrast as an effective means to robust clustering against varying densities. Machine Learning, https://doi.org/10.1007/s10994-017-5693-x.

9.    Yue Zhu, Kai Ming Ting, Zhi-Hua Zhou (2018). Multi-Label Learning with Emerging New Labels. IEEE Transactions on Knowledge and Data Engineering, Vol 30, Issue 10, 1901-1912, https://doi.org/10.1109/TKDE.2018.2810872.

10.    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. Doi:10.1111/coin.12156.

11.    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. Vol 106, Issue 1, 55-91.

12.    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. Doi:10.1007/s10115-017-1046-0.

13.    Xin Mu, 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, Vol 29, 1605-1618.

14.    Guansong Pang, Kai Ming Ting , David Albrecht, Huidong Jin (2016). ZERO++: Harnessing the power of zero appearances to detect anomalies. Journal of Artificial Intelligence Research . Vol 57, 593-620.

15.    Ye Zhu, Kai Ming Ting, Mark James Carman (2016). Density-ratio based clustering for discovering clusters with varying densities. Pattern Recognition. Vol 60, Issue C, 983-997.

16.    Sunil Aryal and Kai Ming Ting (2016). A generic ensemble approach to estimate multi-dimensional likelihood in Bayesian classifier learning. Computational Intelligence. Vol. 32, Issue 3, 458-479.

17.    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.

18.    Jonathan R. Wells, Kai Ming Ting and Takashi Washio (2014). LiNearN: A New Approach to Nearest Neighbour Density Estimator. Pattern Recognition. Vol.47, Issue 8, 2702-2720. Elsevier.

19.    Kai Ming Ting, Guang-Tong Zhou, Fei Tony Liu and Swee Chuan Tan (2013). Mass Estimation. Machine Learning. Vol.90, Issue.1, 127-160.

20.    Zhouyu Fu, Guojun Lu, Kai Ming Ting and Dengsheng Zhang (2013). Learning Sparse Kernel Classifiers for Multi-Instance Classification. IEEE Transactions on Neural Networks and Learning Systems. Vol.24, Issue 9, 1377-1389.

21.    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. Vol.35, Issue 3, 493-524. Springer.

22.    Guang-Tong Zhou, Kai Ming Ting, Fei Tony Liu and Yilong Yin (2012). Relevance Feature Mapping for Content-based Multimedia Information Retrieval. Pattern Recognition. Vol.45: 1707-1720.

23.    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. DOI: acm.org/10.1145/2133360.2133363.

24.    Zhouyu Fu, Guojun Lu, Kai Ming Ting and Dengsheng Zhang (2011). A Survey of Audio-based Music Classification and Annotation. IEEE Transactions on Multimedia. Vol.14, Issue.2, 303-319.

25.    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.

26.    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.

27.    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.

28.    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. Vol.19. No.12. 1652-1665.

29.    Kai Ming Ting (2002). An Instance-Weighting Method to Induce Cost-Sensitive Trees. IEEE Transaction on Knowledge and Data Engineering. Vol. 14, No. 3. 659-665.

30.    Kai Ming Ting and Ian H. Witten (1999). Issues in Stacked Generalization. Journal of Artificial Intelligence Research. AI Access Foundation and Morgan Kaufmann Publishers, Vol.10, 271-289.

Conference Publications

31.    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.

32.    Xiaoyu 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, 2019.

33.    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.

34.    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.

35.    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.

36.    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.

37.    Yue 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.

38.    Yue Zhu, Kai Ming Ting, Zhi-Hua Zhou (2017). Discover Multiple Novel Labels in Multi-Instance Multi-Label Learning. Proceedings of the 2017 Association for the Advancement of Artificial Intelligence (AAAI). 2977-2984.

39.    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.

40.    Yue 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.

41.    Sunil Aryal, Kai Ming Ting, Gholamreza Haffari and Takashi Washio (2015). Beyond tf-idf and cosine distance in documents dissimilarity measure. Proceedings of Asia Information Retrieval Societies Conference. 363-368.

42.    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.

43.    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.

44.    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.

45.    Zhouyu Fu, Guojun Lu, Kai Ming Ting and Dengsheng Zhang (2013). Learning Optimal Cepstral Features for Audio Classification. Proceedings of the International Joint Conference on Artificial Intelligence. 1330-1336.

46.    Zhouyu Fu, Guojun Lu, Kai Ming Ting and Dengsheng Zhang (2012). Learning Sparse Kernel Classifiers in the Primal. Proceedings of International Workshop on Structural, Syntactical, and Statistical Pattern Recognition. 60-69.

47.    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.

48.    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.

49.    Zhouyu Fu, Guojun Lu, Kai Ming Ting and Dengsheng Zhang (2011). Building Sparse Support Vector Machines for Multi-Instance Classification. Proceedings of European Conference on Machine Learning. 471-486.

50.    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.

51.    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.

52.    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.

53.    Zhouyu Fu, Guojun Lu, Kai Ming Ting and Dengsheng Zhang (2010). On Feature Combination for Music Classification. Proceedings of International Workshop on Structural, Syntactical & Statistical Pattern Recognition. 453-462.

54.    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.  [Received the runner-up best paper award]

55.    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.

56.    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. [Received the best paper award]

57.    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

58.    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.

59.    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.

60.    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.

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