Professor, School of Artificial Intelligence
Nanjing University, Xianlin Campus Mailbox 603
Email: tingkm at nju.edu.cn
·
Isolation kernel
·
Mass-based similarity
·
Mass estimation and mass-based
approaches
·
Ensemble approaches
·
Data stream data mining
·
Machine learning
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.
·
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
·
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.
·
“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.
·
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
(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:.
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.
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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