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Kai Ming Ting: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Tasadduq Imam, Kai Ming Ting, Joarder Kamruzzaman
    z-SVM: An SVM for Improved Classification of Imbalanced Data. [Citation Graph (0, 0)][DBLP]
    Australian Conference on Artificial Intelligence, 2006, pp:264-273 [Conf]
  2. Kwok Pan Pang, Kai Ming Ting
    Improving the Centered CUSUMS Statistic for Structural Break Detection in Time Series. [Citation Graph (0, 0)][DBLP]
    Australian Conference on Artificial Intelligence, 2004, pp:402-413 [Conf]
  3. Ying Yang, Kevin B. Korb, Kai Ming Ting, Geoffrey I. Webb
    Ensemble Selection for SuperParent-One-Dependence Estimators. [Citation Graph (0, 0)][DBLP]
    Australian Conference on Artificial Intelligence, 2005, pp:102-112 [Conf]
  4. Kai Ming Ting
    A Study on the Effect of Class Distribution Using Cost-Sensitive Learning. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2002, pp:98-112 [Conf]
  5. Kai Ming Ting, Zijian Zheng
    Boosting Cost-Sensitive Trees. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 1998, pp:244-255 [Conf]
  6. Kai Ming Ting
    An Empirical Study of MetaCost Using Boosting Algorithms. [Citation Graph (0, 0)][DBLP]
    ECML, 2000, pp:413-425 [Conf]
  7. Kai Ming Ting
    Matching Model Versus Single Model: A Study of the Requirement to Match Class Distribution Using Decision Trees. [Citation Graph (0, 0)][DBLP]
    ECML, 2004, pp:429-440 [Conf]
  8. Kai Ming Ting, Boon Toh Low
    Model Combination in the Multiple-Data-Batches Scenario. [Citation Graph (0, 0)][DBLP]
    ECML, 1997, pp:250-265 [Conf]
  9. Kai Ming Ting, Zijian Zheng
    Boosting Trees for Cost-Sensitive Classifications. [Citation Graph (0, 0)][DBLP]
    ECML, 1998, pp:190-195 [Conf]
  10. 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. [Citation Graph (0, 0)][DBLP]
    ECML, 2006, pp:533-544 [Conf]
  11. Kai Ming Ting
    An M-of-N Rule Induction Algorithm and its Application to DNA Domain. [Citation Graph (0, 0)][DBLP]
    HICSS (5), 1994, pp:133-140 [Conf]
  12. Kai Ming Ting
    Towards using a Single Uniform Metric in Instance-Based Learning. [Citation Graph (0, 0)][DBLP]
    ICCBR, 1995, pp:559-568 [Conf]
  13. 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. [Citation Graph (0, 0)][DBLP]
    ICDM, 2003, pp:653-656 [Conf]
  14. Kai Ming Ting
    A Comparative Study of Cost-Sensitive Boosting Algorithms. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:983-990 [Conf]
  15. Kai Ming Ting
    Issues in Classifier Evaluation using Optimal Cost Curves. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:642-649 [Conf]
  16. Kai Ming Ting
    The Characterisation of Predictive Accuracy and Decision Combination. [Citation Graph (0, 0)][DBLP]
    ICML, 1996, pp:498-506 [Conf]
  17. Kai Ming Ting, Ian H. Witten
    Stacking Bagged and Dagged Models. [Citation Graph (0, 0)][DBLP]
    ICML, 1997, pp:367-375 [Conf]
  18. Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting
    Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees. [Citation Graph (0, 0)][DBLP]
    ICML, 1999, pp:493-502 [Conf]
  19. Kai Ming Ting, Ian H. Witten
    Stacked Generalizations: When Does It Work? [Citation Graph (0, 0)][DBLP]
    IJCAI (2), 1997, pp:866-873 [Conf]
  20. Fei Tony Liu, Kai Ming Ting
    Variable Randomness in Decision Tree Ensembles. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2006, pp:81-90 [Conf]
  21. Fei Tony Liu, Kai Ming Ting, Wei Fan
    Maximizing Tree Diversity by Building Complete-Random Decision Trees. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2005, pp:605-610 [Conf]
  22. Kai Ming Ting, Zijian Zheng
    Improving the Performance of Boosting for Naive Bayesian Classification. [Citation Graph (0, 0)][DBLP]
    PAKDD, 1999, pp:296-305 [Conf]
  23. Kai Ming Ting
    Inducing Cost-Sensitive Trees via Instance Weighting. [Citation Graph (0, 0)][DBLP]
    PKDD, 1998, pp:139-147 [Conf]
  24. Kai Ming Ting
    Discretisation in Lazy Learning Algorithms. [Citation Graph (0, 0)][DBLP]
    Artif. Intell. Rev., 1997, v:11, n:1-5, pp:157-174 [Journal]
  25. Kai Ming Ting
    Decision Combination Based on the Characterisation of Predictive Accuracy. [Citation Graph (0, 0)][DBLP]
    Intell. Data Anal., 1997, v:1, n:1-4, pp:181-205 [Journal]
  26. Kai Ming Ting, Ian H. Witten
    Issues in Stacked Generalization. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 1999, v:10, n:, pp:271-289 [Journal]
  27. Kai Ming Ting, Boon Toh Low, Ian H. Witten
    Learning from Batched Data: Model Combination Versus Data Combination. [Citation Graph (0, 0)][DBLP]
    Knowl. Inf. Syst., 1999, v:1, n:1, pp:83-106 [Journal]
  28. Geoffrey I. Webb, Kai Ming Ting
    On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2005, v:58, n:1, pp:25-32 [Journal]
  29. Kai Ming Ting
    An Instance-Weighting Method to Induce Cost-Sensitive Trees. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Knowl. Data Eng., 2002, v:14, n:3, pp:659-665 [Journal]
  30. Swee Chuan Tan, Kai Ming Ting, Shyh Wei Teng
    Examining Dissimilarity Scaling in Ant Colony Approaches to Data Clustering. [Citation Graph (0, 0)][DBLP]
    ACAL, 2007, pp:269-280 [Conf]
  31. 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. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Knowl. Data Eng., 2007, v:19, n:12, pp:1652-1665 [Journal]

  32. Ehipasiko: A Content-based Image Indexing and Retrieval System. [Citation Graph (, )][DBLP]


  33. Cocktail Ensemble for Regression. [Citation Graph (, )][DBLP]


  34. Isolation Forest. [Citation Graph (, )][DBLP]


  35. Mass estimation and its applications. [Citation Graph (, )][DBLP]


  36. Boosting Support Vector Machines Successfully. [Citation Graph (, )][DBLP]


  37. FaSS: Ensembles for Stable Learners. [Citation Graph (, )][DBLP]


  38. On Detecting Clustered Anomalies Using SCiForest. [Citation Graph (, )][DBLP]


  39. On Feature Combination for Music Classification. [Citation Graph (, )][DBLP]


  40. Issues of grid-cluster retrievals in swarm-based clustering. [Citation Graph (, )][DBLP]


  41. A Study of AdaBoost with Naive Bayesian Classifiers: Weakness and Improvement. [Citation Graph (, )][DBLP]


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