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Recent Publications by Kai Ming Ting |
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Journal Publications |
Liu, T., Ting, K.M., Yu, Y. & Zhou, Z. (2008). Spectrum of Variable-Random Trees. Journal of Artificial Intelligence Research . Vol.32. pp. 355-384. AI Access Foundation.
Yang, Y., Webb, G.I., J., Korb, K., and Ting, K.M. Classifying under computational resource constraints: anytime classification using probabilistic estimators. Machine Learning . Vol.69. No.1. September 2007, pp. 35-53. Springer.
Yang, Y., Webb, G.I., Cerquides, J., Korb, K., Boughton, J. and Ting, K.M. To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Ensembles. IEEE Transaction on Knowledge and Data Engineering . Vol.19. No.12. December 2007, pp. 1652-1665.
Webb, G.I. and Ting, K.M. On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions. Machine Learning Journal , Vol.58. No.1. January 2005, pp. 25-32. Kluwer Academic Publishers. ISSN: 0885-6125
Ting, K.M. and Zheng , Z. A Study of AdaBoost with Naïve Bayesian Classifiers: Weakness and Improvement. International Journal of Computational Intelligence . Vol. 19, No. 2, May 2003, pp. 186-200. Blackwell Publishing.
Ting, K.M.. An Instance-Weighting Method to Induce Cost-Sensitive Trees. IEEE Transaction on Knowledge and Data Engineering . Vol. 14, No. 3, pp. 659-665, May/June 2002.
Ting, K.M. and
Ting, K.M., Low, B.T. and
Ting, K.M. Decision Combination based on the Characterisation of Predictive Accuracy. International Journal of Intelligent Data Analysis . 1997, Vol. 1, No. 3. pp. 181-205. Elsevier Science.
Ting, K.M. Discretisation in Lazy Learning Algorithms. Artificial Intelligence Review . 1997, Vol. 11, pp. 157-174. (Special Issue on `Lazy Learning'). Kluwer Academic Publishers.
Book Chapters
Beliakov , G., Ting, K.M., Murshed , M., Rubinov , A. and Bertoli , M. Efficient Serial and Parallel Implementations of the Cutting Angle Global Optimisation Technique. Chapter 3 in High Performance Algorithms and Software for Nonlinear Optimization , G. Di Pillo and A. Murli , editors, G. Kluwer Academic Publishers. 2003.
Ting, K.M. Cost Sensitive Classification Using Decision Trees, Boosting and MetaCost . Book chapter in Heuristic and Optimization for Knowledge Discovery . Edited by Sarker , R., Abbass , H. & Newton, C. Idea Group Publishing. 2002.
Ting, K.M. Discretisation in lazy learning algorithms, Lazy Learning . Editor: David Aha. Kluwer Academic Publishers. 1997. A reprint from Journal of Artificial Intelligence Review.
Conference Publications
Liu, T., Ting, K.M. & Zhou,
Z-H. (2008). Isolation
Forests. Proceedings
of the 2008 IEEE International Conference on Data Mining. 15-19
December 2008.
Tan, S., Ting, K.M., & Teng , S. (2008). Issues of Grid-cluster
Retrievals in Swarm-based clustering. Proceedings of the 2008 IEEE World
Congress on Computational Intelligence - Congress on Evolutionary Computation . 1
June 2008 to 6 June 2008,
Yu, Y., Zhou, Z-H. & Ting, K.M. Cocktail
Ensemble for Regression. Proceedings of the 2007 IEEE International Conference
on Data Mining . October 28-31, 2007
.
Tan, S.C., Ting, K.M. and Teng, S.W. Examining
Dissimilarity Scaling in Ant Colony Approaches to Data Clustering. Proceedings
of The Third Australian Conference on Artificial Life.
4-6 December 2007.
Liu, T & Ting, K.M. Variable Randomness in Decision
Tree Ensembles. Proceedings of the Tenth Pacific-Asia Conference on
Knowledge Discovery and Data Mining . April 9-12, 2006 .
Tan, S.C., Ting, K.M. and Teng, S.W. Reproducing the results of ant-based clustering without
using ants. Proceedings of IEEE Congress on Evolutionary Computation (CEC 2006) . Pages. 1760- 1767.
Yang, Y., Webb, G.I., Cerquides, J., Korb, K., Boughton, J. and Ting, K.M. To Select or To Weigh: A Comparative Study of Model Selection and Model Weighing for SPODE Ensembles. Lecture Notes in Computer Science 4212. Proceedings of the 17th European Conference on Machine Learning (ECML 2006). Pages. 533-544. Springer.
Imam, T., Ting, K.M. & Kamruzzaman, J. z-SVM: An SVM for Improved Classification of Imbalanced Data. Lecture Notes in Computer Science 4304: Advances in Artificial Intelligence. Proceedings of the 19th Australian Joint Conference on Artificial Intelligence (AI 2006) , pages 264-273. Springer.
Teng, S. W. & Ting K.M. Ehipasiko: A Content-Based Image Indexing and Retrieval System. Proceedings of 4th International Conference on Active MediaTechnology . 7-9 June, 2006, pages. 436-437, IOS Press.
Molloy, S., Albrecht, D., Dowe , D. & Ting, K.M. Model-based Clustering of Sequential
Data. Proceedings of the 5th Annual
Liu, T. F., Ting, K.M. and Fan, W. 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. pp. 605-610. May 2005.
Pang, K.P. and Ting, K.M. SUMSRM: A New Statistic for the Structural Break
Detection in Time Series. In Proceedings of the 2005
Yang, Y., K. Korb , K-M. Ting, and G.I. Webb (2005). Ensemble
Selection for SuperParent -One-Dependence Estimators.
In Lecture Notes in Computer Science: Proceedings of the 18th Australian
Conference on AI (AI 05) ,
volume LNCS 3809, pages 102-111.
Ting, K.M. Matching Model versus Single Model: A Study of the Requirement to
Match Class Distribution using Decision Trees. In Proceedings of the
Fifteenth European Conference in Machine Learning .
Pang, K.P. and Ting, K.M. Improving the Centered CUSUMS Statistics for Structural Break Detection
in Time Series. In Proceedings of The
Seventeenth Australian Joint Artificial Intelligence Conference.
Ting, K.M and Quek , R.J.Y. Model Stability: A key factor in determining
whether an algorithm produces an optimal model from a matching distribution. Proceedings
of the Third International Conference on Data Mining . pp. 653-656.
Ting, K.M. Issues in
Classifier Evaluation using Optimal Cost Curves . Proceedings
of The Nineteenth International Conference on Machine Learning
. pp. 642-649.
Ting, K.M. A Study of
the Effect of Class Distribution using Cost-Sensitive Learning
. Proceedings of the Fifth International Conference on Discovery
Science .
pp. 98-112. LNCS-2534.
Wells, J.R., Ting, K.M., Murshed , M., and Beliakov , G. Analysis of Runtime Performance using Heap Data Structure for the Cutting Angle Global Optimisation Algorithm. Proceedings of the Sixth International Conference on Computer and Information Technology . pp. 80-85. December 19-21, 2003.
Pang, K.P. and Ting, K.M. Improving Time Series Prediction
by Data Selection. Proceedings of International
Conference on Computational Intelligence for Modeling
Control and Automation.
Beliakov , G., Ting, K.M. and Murshed ,
M. Efficient Serial and Parallel Implementations of the Cutting Angle
Global Optimisation Technique. Proceedings of the Fifth International
Conference on Optimization: Techniques and Applications. Vol. 1, pp.
80-87. December 15-17, 2001.
Barnes, M.B., Ting, K.M. and Rimmer , R.J. Predicting stock indices n-days ahead: A
comparison of techniques using Australian data. Proceedings of the
International ICSC-NAISO Congress on Computational Intelligence
:Methods and Applications.
Ting, K.M. A Comparative
Study of Cost-Sensitive Boosting Algorithms . Proceedings
of The Seventeenth International Conference on Machine
Learning. pp. 983-990.
Ting, K.M. An Empirical Study of MetaCost using
Boosting Algorithms. Proceedings of The Eleventh
European Conference on Machine Learning. LNAI-1810, pp. 413-425.
Barnes, M.B., Rimmer , R.J. and Ting, K.M. A study of
techniques for mining data from the Australian Stock Exchange . Proceedings
of The Fourth World Multiconference on Systemics ,
Cybernetics and Informatics , SCI 2000 Vol. VIII Part II pp.52-57. July 23
- 26, 2000.
Ting, K.M., Zheng , Z. and Webb, G. Learning Lazy Rules to Improve the Performance of Classifiers , Proceedings of the Nineteenth SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence , Springer, pp. 122-131, 1999.
Wan, C., Wang, L. and Ting, K.M. Introducing
Cost-Sensitive Neural Networks , Proceedings
of The Second International Conference on Information, Communications &
Signal Processing (ICICS) .
Zheng , Z., Webb, G. and Ting, K.M. Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees . Proceedings of the Sixteenth International Conference on Machine Learning. 1999, pp. 493-502, Morgan Kaufmann.
Frayman
, Y., Ting, K.M. and Wang, L. A Fuzzy
Neural Network for Data Mining: Dealing with the Problem of Small Disjuncts . Proceedings of the
International Joint Conference on Neural Networks (IJCNN'99). paper 543,
Ting, K.M. and Z. Zheng . Improving the Performance of Boosting for Naive Bayesian
Classification. Proceedings of the Third
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-99).
LNAI-1574,
Ting, K.M. and Z. Zheng . Boosting
Cost-Sensitive Trees . P roceedings
of the First International Conference on Discovery Science . LNAI-1532,
Z. Zheng , G.I. Webb, and K.M. Ting. Integrating Boosting and Stochastic Attribute Selection Committees for Further Improving the Performance of Decision Tree Learning . P roceedings of the 10th IEEE International Conference on Tools with Artificial Intelligence (ICTAI-98), IEEE Computer Society Press, pp. 216-223, 1998.
Ting, K.M. Inducing Cost-Sensitive Trees via Instance Weighting . Proceedings of The Second European Symposium on Principles of Data Mining and Knowledge Discovery. LNAI-1510, pp. 139-147, 1998.
Ting, K.M. and Z.. Zheng . Boosting
Trees for Cost-Sensitive Classifications . Proceedings
of the Tenth European Conference on Machine Learning . LNAI-1398,
Ting, K.M. and B.T. Low. Model
Combination in the multiple-data-batches scenario .
Proceedings of the Ninth European Conference on Machine
Learning. LNAI-1224, 1997, pp. 250-265. ( A old version appears as Working
Paper 96/19 . Department of Computer Science,
Ting, K.M. and I.H.
Ting, K.M. and I.H.
Ting, K.M. The Characterisation of Predictive Accuracy and Decision Combination . Proceedings of the Thirteen International Conference on Machine Learning . 1996, pp. 498-506.
Ting, K.M. Towards using a Single Uniform
Metric in Instance-Based Learning. Proceedings of the First International
Conference on Case-Based Reasoning .
Ting, K.M. An
M-of-N Rule Induction Algorithm and its Application to DNA Domain
. P roceedings of the
Twenty-Seventh Annual
Ting, K.M. Discretization of Continuous-valued attributes and Instance-Based Learning , Technical Report No. 491, 1994, Basser Department of Computer Science, The University of Sydney.
Ting, K.M. and M.R. Cameron-Jones. Exploring a Framework for Instance-Based Learning and Naive Bayesian Classifiers . Proceedings of the Seventh Australian Joint Conference on Artificial Intelligence . 1994, pp. 100-107.
Ting, K.M. The
Problem of Atypicality in Instance-Based Learning , Proceedings of the Third
Ting, K.M. The Problem of Small Disjuncts : its remedy in Decision Trees , Proceedings of the Tenth Canadian Conference on Artificial Intelligence , 1994, pp. 91-97.
Last Update: September 2008. This is a personal page maintained by the author. Disclaimer