Wrapper-based computation and evaluation of sampling methods for imbalanced datasets

Proceedings of the 1st International Workshop on Utility-Based Data Mining, UBDM '05

Volumn , Issue , 2005, Pages 24-33

  Chawla, Nitesh V ^a   Hall, Lawrence O ^b   Joshi, Ajay ^b  

^a University of Notre Dame   (United States)

^b University of South Florida   (United States)

Author keywords

cost sensitive learning and evaluation; imbalanced datasets; SMOTE; under sampling; wrapper

Indexed keywords

AVERAGE COST; CLASSIFICATION ACCURACY; COST CURVES; COST-SENSITIVE LEARNING; DATA SETS; EVALUATION FUNCTION; LARGE SCALE SIMULATIONS; REGIONS OF INTEREST; SAMPLING METHOD; SYNTHETIC GENERATION; TEST EXAMPLES; TRUE POSITIVE RATES; UNDER-SAMPLING; WRAPPER APPROACH;

DATA MINING; INTRUSION DETECTION; STATISTICAL TESTS;

COST REDUCTION;

EID: 51149119276     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1089827.1089830     Document Type: Conference Paper

References (34)

1. In T. Dietterich, D. Margineantu, F. Provost, and P. Turney, editors, Proceedings of the ICML'2000 Workshop on COST-SENSITIVE LEARNING. 2003.
2. In N. V. Chawla, N. Japkowicz, and A. Kołlcz, editors, Proceedings of the ICML'2003 Workshop on Learning from Imbalanced Data Sets. 2003.
3. In N. V. Chawla, N. Japkowicz, and A. Kolcz, editors, SIGKDD Explorations Special Issue on Learning from Imbalanced Datasets. SIGKDD, 2004.
4. In C. Ferri, P. Flach, J. Orallo, and N. Lachice, editors, ECAI' 2004 First Workshop on ROC Analysis in AI. ECAI, 2004.
5. N. B. Amor, S. Benferhat, and Z. Elouedi. Naive Bayes vs. Decision Trees in Intrusion Detection Systems. In Proceedings of the ACM Symposium on Applied Computing, pages 420-424, 2004.
6. R. E. Banfield, L. O. Hall, K. W. Bowyer, and W. P. Kegelmeyer. Ensembles of Classifiers from Spatially Disjoint Data. In Proceedings of the Sixth International Conference on Multiple Classifier Systems, 2005.
7. G. E. A. P. A. Batista, R. C. Prati, and M. C. Monard. A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explorations, 6(1), 2004.
8. C. Blake and C. Merz. UCI Repository of Machine Learning Databases. Department of Information and Computer Sciences, University of California, Irvine, 1998.
9. K. W. Bowyer, L. O. Hall, N. V. Chawla, and T. E. Moore. A parallel Decision Tree Builder for Mining Very Large Visualization Datasets. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2000.
10. M. Buckland and F. Gey. The Relationship Between Recall and Precision. Journal of the American Society for Information Science, 45:12-19, 1994.
11. P. Chan, W. Fan, P. Prodromodis, and S. Stolfo. Distributed Data Mining in Credit Card Fraud Detection. IEEE Intelligent Systems, 14(6):67-74, 1999.
12. N. V. Chawla. C4.5 and imbalanced datasets: Investigating the effect of ampling method, probabilistic estimate, and decision tree structure. In Proceedings of the ICML'03 Workshop on Class Imbalances, 2003.
13. N. V. Chawla. Editorial: Learning from Imbalanced Datasets. SIGKDD Explorations, 6(1), 2004.
14. N. V. Chawla, L. O. Hall, B. K. W., and W. P. Kegelmeyer. SMOTE: Synthetic Minority Oversampling TEchnique. Journal of Artificial Intelligence Research, 16:321-357, 2002.
15. W. W. Cohen. Fast Effective Rule Induction. In Proc. 12th International Conference on Machine Learning, pages 115-123, Lake Tahoe, CA, 1995. Morgan Kaufmann.
16. C. Drummond and R. Holte. Explicitly representing expected cost: An alternative to ROC representation. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 198-207, 2001.
17. C. Drummond and R. Holte. C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling. In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets, 2003.
18. C. Elkan. Results of the kdd'99 Classifier Learning Contest. http://www.cse.ucsd.edu/~elkan/clresults.html, 1999.
19. J. Ezawa, K., M. Singh, and W. Norton, S. Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management. In Proceedings of the International Conference on Machine Learning, ICML-96, pages 139-147, Bari, Italy, 1996. Morgan Kauffman.
20. S. Hettich and S. D. Bay. The UCI KDD Archive [http://kdd.ics.uci.edu]. Department of Information and Computer Sciences, University of California, Irvine, 1998.
21. N. Japkowicz. The Class Imbalance Problem: Significance and Strategies. In Proceedings of the 2000 International Conference on Artificial Intelligence (IC-AI'2000): Special Track on Inductive Learning, Las Vegas, Nevada, 2000.
22. N. Japkowicz and S. Stephen. The Class Imbalance Problem: A Systematic Study. Intelligent Data Analysis, 6(5):203-231, 2002.
23. R. Kohavi and G. H. John. Wrappers for feature subset selection. Artificial Intelligence, 97(1-2):273-324, 1997.
24. M. Kubat, R. Holte, and S. Matwin. Machine Learning for the Detection of Oil Spills in Satellite Radar Images. Machine Learning, 30:195-215, 1998.
25. M. Kubat and S. Matwin. Addressing the Curse of Imbalanced Training Sets: One Sided Selection. In Proceedings of the Fourteenth International Conference on Machine Learning, pages 179-186, Nashville, Tennesse, 1997. Morgan Kaufmann.
26. B. S. Lee, R. R. Snapp, and R. Musick. Toward a Query Language on Simulation Mesh Data: An Object Oriented Approach. In Proceedings of the International Conference on Database Systems for Advanced Applications, 2001.
27. C. Ling and C. Li. Data Mining for Direct Marketing Problems and Solutions. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), New York, NY, 1998. AAAI Press.
28. M. Maloof. Learning when data sets are imbalanced and when costs are unequal and unknown. In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets, 2003.
29. F. Provost and T. Fawcett. Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, pages 43-48, New Port Beach, CA, 1997. AAAI Press.
30. J. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1992.
31. M. R. Sabhnani and G. Serpen. Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset with Misuse Detection Context. In Proceedings of the International Conference on Machine Learning: Models, Technologies, and Applications, pages 209-215, 2003.
32. G. Weiss and F. Provost. Learning when Training Data are Costly: The Effect of Class Distribution on Tree Induction. Journal of Artificial Intelligence Research, 19:315-354, 2003.
33. K.Woods, C. Doss, K. Bowyer, J. Solka, C. Priebe, and P. Kegelmeyer. Comparative Evaluation of Pattern Recognition Techniques for Detection of Microcalcifications in Mammography. International Journal of Pattern Recognition and Artificial Intelligence, 7(6):1417-1436, 1993.
34. B. Zadrozny and C. Elkan. Learning and Making Decisions When Costs and Probabilities are Both Unknown. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 204-213, 2001.