Kai Ming Ting

Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Fei Tony Liu, Sunil Aryal: DEMass: a new density estimator for big data. Knowl. Inf. Syst. 35(3): 493-524 (2013)

Kai Ming Ting, Guang-Tong Zhou, Fei Tony Liu, Swee Chuan Tan: Mass estimation. Machine Learning 90(1): 127-160 (2013)

Sunil Aryal, Kai Ming Ting: MassBayes: A New Generative Classifier with Multi-dimensional Likelihood Estimation. PAKDD (1) 2013: 136-148

Geoffrey I. Webb, Janice R. Boughton, Fei Zheng, Kai Ming Ting, Houssam Salem: Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification. Machine Learning 86(2): 233-272 (2012)

Guang-Tong Zhou, Kai Ming Ting, Fei Tony Liu, Yilong Yin: Relevance feature mapping for content-based multimedia information retrieval. Pattern Recognition 45(4): 1707-1720 (2012)

Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou: Isolation-Based Anomaly Detection. TKDD 6(1): 3 (2012)

Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang: Learning Sparse Kernel Classifiers in the Primal. SSPR/SPR 2012: 60-69

Kai Ming Ting, Jonathan R. Wells, Swee Chuan Tan, Shyh Wei Teng, Geoffrey I. Webb: Feature-subspace aggregating: ensembles for stable and unstable learners. Machine Learning 82(3): 375-397 (2011)

Swee Chuan Tan, Kai Ming Ting, Shyh Wei Teng: A general stochastic clustering method for automatic cluster discovery. Pattern Recognition 44(10-11): 2786-2799 (2011)

Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang: Music classification via the bag-of-features approach. Pattern Recognition Letters 32(14): 1768-1777 (2011)

Swee Chuan Tan, Kai Ming Ting, Shyh Wei Teng: Simplifying and improving ant-based clustering. Procedia CS 4: 46-55 (2011)

Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang: A Survey of Audio-Based Music Classification and Annotation. IEEE Transactions on Multimedia 13(2): 303-319 (2011)

Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Fei Tony Liu: Density Estimation Based on Mass. ICDM 2011: 715-724

Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang: On Low-Rank Regularized Least Squares for Scalable Nonlinear Classification. ICONIP (2) 2011: 490-499

Swee Chuan Tan, Kai Ming Ting, Fei Tony Liu: Fast Anomaly Detection for Streaming Data. IJCAI 2011: 1511-1516

Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang: Building Sparse Support Vector Machines for Multi-Instance Classification. ECML/PKDD (1) 2011: 471-486

Kai Ming Ting, Ian H. Witten: Issues in Stacked Generalization. CoRR abs/1105.5466 (2011)

Takashi Washio, Einoshin Suzuki, Kai Ming Ting: Best papers from the 12th Pacific-Asia conference on knowledge discovery and data mining (PAKDD2008). Knowl. Inf. Syst. 25(2): 209-210 (2010)

Swee Chuan Tan, Kai Ming Ting, Shyh Wei Teng: A Comparative Study of a Practical Stochastic Clustering Method with Traditional Methods. Australasian Conference on Artificial Intelligence 2010: 112-121

Kai Ming Ting, Jonathan R. Wells: Multi-dimensional Mass Estimation and Mass-based Clustering. ICDM 2010: 511-520

Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang: Learning Naive Bayes Classifiers for Music Classification and Retrieval. ICPR 2010: 4589-4592

Kai Ming Ting, Guang-Tong Zhou, Fei Tony Liu, James Swee Chuan Tan: Mass estimation and its applications. KDD 2010: 989-998

Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou: On Detecting Clustered Anomalies Using SCiForest. ECML/PKDD (2) 2010: 274-290

Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang: On Feature Combination for Music Classification. SSPR/SPR 2010: 453-462

Kai Ming Ting: Confusion Matrix. Encyclopedia of Machine Learning 2010: 209

Kai Ming Ting: Error Rate. Encyclopedia of Machine Learning 2010: 331

Kai Ming Ting: Precision. Encyclopedia of Machine Learning 2010: 780

Kai Ming Ting: Precision and Recall. Encyclopedia of Machine Learning 2010: 781

Kai Ming Ting: Sensitivity and Specificity. Encyclopedia of Machine Learning 2010: 901-902

Kai Ming Ting, Jonathan R. Wells, Swee Chuan Tan, Shyh Wei Teng, Geoffrey I. Webb: FaSS: Ensembles for Stable Learners. MCS 2009: 364-374

Kai Ming Ting, Lian Zhu: Boosting Support Vector Machines Successfully. MCS 2009: 509-518

Fei Tony Liu, Kai Ming Ting, Yang Yu, Zhi-Hua Zhou: Spectrum of Variable-Random Trees. J. Artif. Intell. Res. (JAIR) 32: 355-384 (2008)

Swee Chuan Tan, Kai Ming Ting, Shyh Wei Teng: Issues of grid-cluster retrievals in swarm-based clustering. IEEE Congress on Evolutionary Computation 2008: 511-518

Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou: Isolation Forest. ICDM 2008: 413-422

Takashi Washio, Einoshin Suzuki, Kai Ming Ting, Akihiro Inokuchi (Eds.): Advances in Knowledge Discovery and Data Mining, 12th Pacific-Asia Conference, PAKDD 2008, Osaka, Japan, May 20-23, 2008 Proceedings. Lecture Notes in Computer Science 5012, Springer 2008, isbn 978-3-540-68124-3

Ying Yang, Geoffrey I. Webb, Kevin B. Korb, Kai Ming Ting: Classifying under computational resource constraints: anytime classification using probabilistic estimators. Machine Learning 69(1): 35-53 (2007)

Ying Yang, Geoffrey I. Webb, Jesús Cerquides, Kevin B. Korb, Janice R. Boughton, Kai Ming Ting: To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators. IEEE Trans. Knowl. Data Eng. 19(12): 1652-1665 (2007)

Swee Chuan Tan, Kai Ming Ting, Shyh Wei Teng: Examining Dissimilarity Scaling in Ant Colony Approaches to Data Clustering. ACAL 2007: 269-280

Yang Yu, Zhi-Hua Zhou, Kai Ming Ting: Cocktail Ensemble for Regression. ICDM 2007: 721-726

Shyh Wei Teng, Kai Ming Ting: Ehipasiko: A Content-based Image Indexing and Retrieval System. AMT 2006: 436-437

Tasadduq Imam, Kai Ming Ting, Joarder Kamruzzaman: z-SVM: An SVM for Improved Classification of Imbalanced Data. Australian Conference on Artificial Intelligence 2006: 264-273

Ying Yang, Geoffrey I. Webb, Jesús Cerquides, Kevin B. Korb, Janice R. Boughton, Kai Ming Ting: To Select or To Weigh: A Comparative Study of Model Selection and Model Weighing for SPODE Ensembles. ECML 2006: 533-544

Fei Tony Liu, Kai Ming Ting: Variable Randomness in Decision Tree Ensembles. PAKDD 2006: 81-90

Geoffrey I. Webb, Kai Ming Ting: On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions. Machine Learning 58(1): 25-32 (2005)

Ying Yang, Kevin B. Korb, Kai Ming Ting, Geoffrey I. Webb: Ensemble Selection for SuperParent-One-Dependence Estimators. Australian Conference on Artificial Intelligence 2005: 102-112

Fei Tony Liu, Kai Ming Ting, Wei Fan: Maximizing Tree Diversity by Building Complete-Random Decision Trees. PAKDD 2005: 605-610

Kwok Pan Pang, Kai Ming Ting: Improving the Centered CUSUMS Statistic for Structural Break Detection in Time Series. Australian Conference on Artificial Intelligence 2004: 402-413

Kai Ming Ting: Matching Model Versus Single Model: A Study of the Requirement to Match Class Distribution Using Decision Trees. ECML 2004: 429-440

Kai Ming Ting, Zijian Zheng: A Study of AdaBoost with Naive Bayesian Classifiers: Weakness and Improvement. Computational Intelligence 19(2): 186-200 (2003)

Kai Ming Ting, Regina Jing Ying Quek: Model Stability: A key factor in determining whether an algorithm produces an optimal model from a matching distribution. ICDM 2003: 653-656

Kai Ming Ting: An Instance-Weighting Method to Induce Cost-Sensitive Trees. IEEE Trans. Knowl. Data Eng. 14(3): 659-665 (2002)

Kai Ming Ting: A Study on the Effect of Class Distribution Using Cost-Sensitive Learning. Discovery Science 2002: 98-112

Kai Ming Ting: Issues in Classifier Evaluation using Optimal Cost Curves. ICML 2002: 642-649

Kai Ming Ting: An Empirical Study of MetaCost Using Boosting Algorithms. ECML 2000: 413-425

Kai Ming Ting: A Comparative Study of Cost-Sensitive Boosting Algorithms. ICML 2000: 983-990

Kai Ming Ting, Ian H. Witten: Issues in Stacked Generalization. J. Artif. Intell. Res. (JAIR) 10: 271-289 (1999)

Kai Ming Ting, Boon Toh Low, Ian H. Witten: Learning from Batched Data: Model Combination Versus Data Combination. Knowl. Inf. Syst. 1(1): 83-106 (1999)

Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting: Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees. ICML 1999: 493-502

Kai Ming Ting, Zijian Zheng: Improving the Performance of Boosting for Naive Bayesian Classification. PAKDD 1999: 296-305

Kai Ming Ting, Zijian Zheng: Boosting Cost-Sensitive Trees. Discovery Science 1998: 244-255

Kai Ming Ting, Zijian Zheng: Boosting Trees for Cost-Sensitive Classifications. ECML 1998: 190-195

Kai Ming Ting: Inducing Cost-Sensitive Trees via Instance Weighting. PKDD 1998: 139-147

Kai Ming Ting: Discretisation in Lazy Learning Algorithms. Artif. Intell. Rev. 11(1-5): 157-174 (1997)

Kai Ming Ting: Decision Combination Based on the Characterisation of Predictive Accuracy. Intell. Data Anal. 1(1-4): 181-205 (1997)

Kai Ming Ting, Boon Toh Low: Model Combination in the Multiple-Data-Batches Scenario. ECML 1997: 250-265

Kai Ming Ting, Ian H. Witten: Stacking Bagged and Dagged Models. ICML 1997: 367-375

Kai Ming Ting, Ian H. Witten: Stacked Generalizations: When Does It Work? IJCAI (2) 1997: 866-873

Kai Ming Ting: The Characterisation of Predictive Accuracy and Decision Combination. ICML 1996: 498-506

Kai Ming Ting: Towards using a Single Uniform Metric in Instance-Based Learning. ICCBR 1995: 559-568

Kai Ming Ting: An M-of-N Rule Induction Algorithm and its Application to DNA Domain. HICSS (5) 1994: 133-140