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Kai Ming Ting

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2008
37 Takashi Washio, Einoshin Suzuki, Kai Ming Ting, Akihiro Inokuchi: Advances in Knowledge Discovery and Data Mining, 12th Pacific-Asia Conference, PAKDD 2008, Osaka, Japan, May 20-23, 2008 Proceedings Springer 2008
36EEFei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou: Isolation Forest. ICDM 2008: 413-422
35EESwee 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
2007
34EESwee Chuan Tan, Kai Ming Ting, Shyh Wei Teng: Examining Dissimilarity Scaling in Ant Colony Approaches to Data Clustering. ACAL 2007: 269-280
33EEYang Yu, Zhi-Hua Zhou, Kai Ming Ting: Cocktail Ensemble for Regression. ICDM 2007: 721-726
32EEYing 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)
31EEYing 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)
2006
30 Shyh Wei Teng, Kai Ming Ting: Ehipasiko: A Content-based Image Indexing and Retrieval System. AMT 2006: 436-437
29EETasadduq Imam, Kai Ming Ting, Joarder Kamruzzaman: z-SVM: An SVM for Improved Classification of Imbalanced Data. Australian Conference on Artificial Intelligence 2006: 264-273
28EEYing 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
27EEFei Tony Liu, Kai Ming Ting: Variable Randomness in Decision Tree Ensembles. PAKDD 2006: 81-90
2005
26EEYing 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
25EEFei Tony Liu, Kai Ming Ting, Wei Fan: Maximizing Tree Diversity by Building Complete-Random Decision Trees. PAKDD 2005: 605-610
24EEGeoffrey 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)
2004
23EEKwok 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
22EEKai Ming Ting: Matching Model Versus Single Model: A Study of the Requirement to Match Class Distribution Using Decision Trees. ECML 2004: 429-440
2003
21EEKai 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
2002
20EEKai Ming Ting: A Study on the Effect of Class Distribution Using Cost-Sensitive Learning. Discovery Science 2002: 98-112
19 Kai Ming Ting: Issues in Classifier Evaluation using Optimal Cost Curves. ICML 2002: 642-649
18EEKai Ming Ting: An Instance-Weighting Method to Induce Cost-Sensitive Trees. IEEE Trans. Knowl. Data Eng. 14(3): 659-665 (2002)
2000
17EEKai Ming Ting: An Empirical Study of MetaCost Using Boosting Algorithms. ECML 2000: 413-425
16 Kai Ming Ting: A Comparative Study of Cost-Sensitive Boosting Algorithms. ICML 2000: 983-990
1999
15 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
14EEKai Ming Ting, Zijian Zheng: Improving the Performance of Boosting for Naive Bayesian Classification. PAKDD 1999: 296-305
13EEKai Ming Ting, Ian H. Witten: Issues in Stacked Generalization. J. Artif. Intell. Res. (JAIR) 10: 271-289 (1999)
12EEKai 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)
1998
11EEKai Ming Ting, Zijian Zheng: Boosting Cost-Sensitive Trees. Discovery Science 1998: 244-255
10 Kai Ming Ting, Zijian Zheng: Boosting Trees for Cost-Sensitive Classifications. ECML 1998: 190-195
9 Kai Ming Ting: Inducing Cost-Sensitive Trees via Instance Weighting. PKDD 1998: 139-147
1997
8 Kai Ming Ting, Boon Toh Low: Model Combination in the Multiple-Data-Batches Scenario. ECML 1997: 250-265
7 Kai Ming Ting, Ian H. Witten: Stacking Bagged and Dagged Models. ICML 1997: 367-375
6 Kai Ming Ting, Ian H. Witten: Stacked Generalizations: When Does It Work? IJCAI (2) 1997: 866-873
5 Kai Ming Ting: Discretisation in Lazy Learning Algorithms. Artif. Intell. Rev. 11(1-5): 157-174 (1997)
4EEKai Ming Ting: Decision Combination Based on the Characterisation of Predictive Accuracy. Intell. Data Anal. 1(1-4): 181-205 (1997)
1996
3 Kai Ming Ting: The Characterisation of Predictive Accuracy and Decision Combination. ICML 1996: 498-506
1995
2 Kai Ming Ting: Towards using a Single Uniform Metric in Instance-Based Learning. ICCBR 1995: 559-568
1994
1 Kai Ming Ting: An M-of-N Rule Induction Algorithm and its Application to DNA Domain. HICSS (5) 1994: 133-140

Coauthor Index

1Janice R. Boughton [28] [32]
2Jesús Cerquides [28] [32]
3Wei Fan [25]
4Tasadduq Imam [29]
5Akihiro Inokuchi [37]
6Joarder Kamruzzaman [29]
7Kevin B. Korb [26] [28] [31] [32]
8Fei Tony Liu [25] [27] [36]
9Boon Toh Low [8] [12]
10Kwok Pan Pang [23]
11Regina Jing Ying Quek [21]
12Einoshin Suzuki [37]
13Swee Chuan Tan [34] [35]
14Shyh Wei Teng [30] [34] [35]
15Takashi Washio [37]
16Geoffrey I. Webb [15] [24] [26] [28] [31] [32]
17Ian H. Witten [6] [7] [12] [13]
18Ying Yang [26] [28] [31] [32]
19Yang Yu [33]
20Zijian Zheng [10] [11] [14] [15]
21Zhi-Hua Zhou [33] [36]

Colors in the list of coauthors

Copyright © Sun May 17 03:24:02 2009 by Michael Ley (ley@uni-trier.de)