Local decomposition for rare class analysis

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2007, Pages 814-823

  Wu, Junjie ^a   Wu, Peng ^a   Chen, Jian ^a   Xiong, Hui ^b  

^a TSINGHUA UNIVERSITY   (China)

^b Rutgers University   (United States)

Author keywords

K means clustering support vector machines; Local clustering; Rare class analysis

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA REDUCTION; LEARNING ALGORITHMS; PROBLEM SOLVING; SUPPORT VECTOR MACHINES;

K-MEANS CLUSTERING SUPPORT VECTOR MACHINES; LOCAL CLUSTERING; RARE CLASS ANALYSIS;

DATABASE SYSTEMS;

EID: 36849083008     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1281192.1281279     Document Type: Conference Paper

References (33)

1. Bmr. In http://www.stat.rutgers.edu/madigan/BMR/.
2. C4.5. In http://www.rulequest.com/Personal/.
3. Kddcup. In http://www.acm.org/sigs/sigkdd/kddcup/index.php.
4. Kddcup99data. In http://kdd.ics.uci.edu//databases/kddcup99/kddcup99. html.
5. Libsvm. In www.csie.ntu.edu.tw/cjlin/libsvm/.
6. N. Chawla, K. Bowyer, L. Hall, and W. Kegelmeyer. Smote: Synthetic minority over-sampling technique. Journal of AI Research, 16:321-357, 2002.
7. W. Cohen. Fast effective rule induction. In ICML, pages 115-123, 1995.
8. N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines (and other kernel-based learning methods). Cambridge University Press, 2000.
9. M. DeGroot and M. Schervish. Probability and Statistics (3 edition). Addison Wesley, 2001.
10. P. Domingos. Metacost: a general method for making classifiers cost-sensitive. In KDD, pages 155-164, 1999.
11. C. Drummond and R. Holte. C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling. In ICML Workshop, 2003.
12. R. Duda, P. Hart, and D. Stork. Pattern classification. Wiley New York, 2001.
13. C. Elkan. The foundations of cost-sensitive learning. In IJCAI, pages 973-978, 2001.
14. W. Fan, S. Stolfo, J. Zhang, and P. Chan. Adacost: misclassification cost-sensitive boosting. In ICML, pages 97-105, 1999.
15. E.-H. Han and et al. Webace: A web agent for document categorization and exploration. In Int'l Conf. on Autonomous Agents, 1998.
16. N. Japkowicz. Supervised learning with unsupervised output separation. In Int 'I Conf on Artificial Intelligence and Soft Computing, pages 321-325, 2002.
17. M. Joshi, R. Agarwal, and V. Kumar. Mining needle in a haystack: Classifying rare classes via two-phase rule induction. In SIGMOD, pages 91-102, 2001.
18. M. Joshi, R. Agarwal, and V. Kumar. Predicting rare classes: Can boosting make any weak learner strong? In KDD, 2002.
19. G. Karypis. Cluto - software for clustering high-dimensional dataseis, version 2.1.1. In http://glaros.dtc.umn.edu/gkhome/views/cluto.
20. M. Kubat, R. Holte, and S. Matwin. Machine learning for the detection of oil spills in satellite radar imaages. Machine Learning, 30:195-215, 1998.
21. M. Kubat and S. Matwin. Addressing the curse of unbalanced training sets: One-sided selection. In ICML, pages 179-186, 1997.
22. C. Ling and C. Li. Data mining for direct marketing: Problems and solutions. In KDD, pages 73-79, 1998.
23. O. Maimon and L. Rokach, editors. The Data Mining and Knowledge Discovery Handbook. Springer, 2005.
24. D. Margineantu and T. Dietterich. Learning decision trees for loss minimization in multi-class problems. In TR 99-30-03. Oregon State University, 1999.
25. P. Murphy and D. Aha. In UCI Repository of Machine Learning Databases. U. of California at Irvine, 1994.
26. D. Newman, S. Hettich, C. Blake, and C. Merz. Uci repository of machine learning databases, 1998.
27. M. F. Porter. An algorithm for suffix stripping. Program, 14(3): 130-137, July 1980.
28. S. Raudys and A. Jain. Small sample size effects in statistical pattern recognition: Recommendations for practitioners. TPAMI, 13(3):252-264, 1991.
29. P.-N. Tan, M. Steinbach, and V. Kumar. Introduction to Data Mining. Addison-Wesley, 2005.
30. TREC. In http://trec.nist.gov.
31. G. Weiss. Mining with rarity: a unifying framework. ACM SIGKDD Explorations, 6(1):7-19, 2004.
32. B. Zadrozny, J. Langford, and N. Abe. Cost-sensitive learning by cost-proportionate example weighting. In ICDM, pages 435-442, 2003.
33. J. Zurada, B. Foster, and T. Ward. Investigation of artificial neural networks for classifying levels of financial distress of firms: The case of an unbalanced training sample. In Knowledge Discovery for Business Information Systems, pages 397-423. Kluwer, 2001.