| 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 |
| 36 | EE | Fei Tony Liu,
Kai Ming Ting,
Zhi-Hua Zhou:
Isolation Forest.
ICDM 2008: 413-422 |
| 35 | EE | 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 |
| 2007 |
|---|
| 34 | EE | Swee Chuan Tan,
Kai Ming Ting,
Shyh Wei Teng:
Examining Dissimilarity Scaling in Ant Colony Approaches to Data Clustering.
ACAL 2007: 269-280 |
| 33 | EE | Yang Yu,
Zhi-Hua Zhou,
Kai Ming Ting:
Cocktail Ensemble for Regression.
ICDM 2007: 721-726 |
| 32 | EE | 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) |
| 31 | EE | 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) |
| 2006 |
|---|
| 30 | | Shyh Wei Teng,
Kai Ming Ting:
Ehipasiko: A Content-based Image Indexing and Retrieval System.
AMT 2006: 436-437 |
| 29 | EE | 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 |
| 28 | EE | 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 |
| 27 | EE | Fei Tony Liu,
Kai Ming Ting:
Variable Randomness in Decision Tree Ensembles.
PAKDD 2006: 81-90 |
| 2005 |
|---|
| 26 | EE | 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 |
| 25 | EE | Fei Tony Liu,
Kai Ming Ting,
Wei Fan:
Maximizing Tree Diversity by Building Complete-Random Decision Trees.
PAKDD 2005: 605-610 |
| 24 | EE | 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) |
| 2004 |
|---|
| 23 | EE | 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 |
| 22 | EE | Kai Ming Ting:
Matching Model Versus Single Model: A Study of the Requirement to Match Class Distribution Using Decision Trees.
ECML 2004: 429-440 |
| 2003 |
|---|
| 21 | EE | 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 |
| 2002 |
|---|
| 20 | EE | Kai 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 |
| 18 | EE | Kai Ming Ting:
An Instance-Weighting Method to Induce Cost-Sensitive Trees.
IEEE Trans. Knowl. Data Eng. 14(3): 659-665 (2002) |
| 2000 |
|---|
| 17 | EE | Kai 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 |
| 14 | EE | Kai Ming Ting,
Zijian Zheng:
Improving the Performance of Boosting for Naive Bayesian Classification.
PAKDD 1999: 296-305 |
| 13 | EE | Kai Ming Ting,
Ian H. Witten:
Issues in Stacked Generalization.
J. Artif. Intell. Res. (JAIR) 10: 271-289 (1999) |
| 12 | EE | 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) |
| 1998 |
|---|
| 11 | EE | Kai 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) |
| 4 | EE | Kai 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 |
