SMOTE: Synthetic minority over-sampling technique

Journal of Artificial Intelligence Research

Volumn 16, Issue , 2002, Pages 321-357

  Chawla, Nitesh V ^a   Bowyer, Kevin W ^b   Hall, Lawrence O ^a   Kegelmeyer, W Philip ^c  

^a University of South Florida   (United States)

^b University of Notre Dame   (United States)

^c SANDIA NATIONAL LABORATORIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); ERROR ANALYSIS; PERFORMANCE; SENSITIVITY ANALYSIS;

MAJORITY CLASSES; OVER SAMPLING; REAL WORLD DATA SETS;

DATA HANDLING;

EID: 0346586663     PISSN: 10769757     EISSN: 10769757     Source Type: Journal    
DOI: 10.1613/jair.953     Document Type: Article

References (37)

1. Blake, C., & Merz, C. (1998). UCI Repository of Machine Learning Databases http://www.ics.uci.edu/~mlearn/~MLRepository.html. Department of Information and Computer Sciences, University of California, Irvine.
2. Bradley, A. P. (1997). The Use of the Area Under the ROC Curve in the Evaluation of Machine Learning Algorithms. Pattern Recognition, 30(6), 1145-1159.
3. Chawla, N., Bowyer, K., Hall, L., & Kegelmeyer, P. (2000). SMOTE: Synthetic Minority Over-sampling TEchnique. In International Conference of Knowledge Based Computer Systems, pp. 46-57. National Center for Software Technology, Mumbai, India, Allied Press.
4. Chawla, N., & Hall, L. (1999). Modifying MUSTAFA to capture salient data. Tech. rep. ISL-99-01, University of South Florida, Computer Science and Eng. Dept.
5. Cohen, W. (1995a). Learning to Classify English Text with ILP Methods. In Proceedings of the 5th International Workshop on Inductive Logic Programming, pp. 3-24. Department of Computer Science, Katholieke Universiteit Leuven.
6. Cohen, W. W. (1995b). Fast Effective Rule Induction. In Proc. 12th International Conference on Machine Learning, pp. 115-123 Lake Tahoe, CA. Morgan Kaufmann.
7. Cohen, W. W., & Singer, Y. (1996). Context-sensitive Learning Methods for Text Categorization. In Frei, H.-P., Harman, D., Schäuble, P., & Wilkinson, R. (Eds.), Proceedings of SIGIR-96, 19th ACM International Conference on Research and Development in Information Retrieval, pp. 307-315 Zürich, CH. ACM Press, New York, US.
8. Cost, S., & Salzberg, S. (1993). A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features. Machine Learning, 10(1), 57-78.
9. DeRouin, E., Brown, J., Fausett, L., & Schneider, M. (1991). Neural Network Training on Unequally Represented Classes. In Intellligent Engineering Systems Through Artificial Neural Networks, pp. 135-141 New York. ASME Press.
10. Domingos, P. (1999). Metacost: A General Method for Making Classifiers Cost-sensitive. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 155-164 San Diego, CA. ACM Press.
11. Drummond, C., & Holte, R. (2000). Explicitly Representing Expected Cost: An Alternative to ROC Representation. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 198-207 Boston. ACM.
12. Duda, R., Hart, P., & Stork, D. (2001). Pattern Classification. Wiley-Interscience.
13. Dumals, S., Platt, J., Heckerman, D., & Sahami, M. (1998). Inductive Learning Algorithms and Representations for Text Categorization. In Proceedings of the Seventh International Conference on Information and Knowledge Management., pp. 148-155.
14. Ezawa, K., J., Singh, M., & Norton, S., W. (1996). Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management. In Proceedings of the International Conference on Machine Learning, ICML-96, pp. 139-147 Bari, Italy. Morgan Kauffman.
15. Fawcett, T., & Provost, F. (1996). Combining Data Mining and Machine Learning for Effective User Profile. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 8-13 Portland, OR. AAAI.
16. Ha, T. M., & Bunke, H. (1997). Off-line, Handwritten Numeral Recognition by Perturbation Method. Pattern Analysis and Machine Intelligence, 19/5, 535-539.
17. Hall, L., Mohney, B., & Kier, L. (1991). The Electrotopological State: Structure Information at the Atomic Level for Molecular Graphs. Journal of Chemical Information and Computer Science, 31(76).
18. Japkowicz, N. (2000). 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.
19. Kubat, M., Holte, R., & Matwin, S. (1998). Machine Learning for the Detection of Oil Spills in Satellite Radar Images. Machine Learning, 30, 195-215.
20. Kubat, M., & Matwin, S. (1997). Addressing the Curse of Imbalanced Training Sets: One Sided Selection. In Proceedings of the Fourteenth International Conference on Machine Learning, pp. 179-186 Nashville, Tennesse. Morgan Kaufmann.
21. Lee, S. (2000). Noisy Replication in Skewed Binary Classification. Computational Statistics and Data Analysis, 34.
22. Lewis, D., & Catlett, J. (1994). Heterogeneous Uncertainity Sampling for Supervised Learning. In Proceedings of the Eleventh International Conference of Machine Learning, pp. 148-156 San Francisco, CA. Morgan Kaufmann.
23. Lewis, D., & Ringuette, M. (1994). A Comparison of Two Learning Algorithms for Text Categorization. In Proceedings of SDAIR-94, 3rd Annual Symposium on Document Analysis and Information Retrieval, pp. 81-93.
24. Ling, C., & Li, C. (1998). 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. AAAI Press.
25. Mladenić, D., &: Grobelnik, M. (1999). Feature Selection for Unbalanced Class Distribution and Naive Bayes. In Proceedings of the 16th International Conference on Machine Learning., pp. 258-267. Morgan Kaufmann.
26. O'Rourke, J. (1998). Computational Geometry in C. Cambridge University Press, UK.
27. Pazzani, M., Merz, C., Murphy, P., Ali, K., Hume, T., & Brunk, C. (1994). Reducing Misclassification Costs. In Proceedings of the Eleventh International Conference on Machine Learning San Francisco, CA. Morgan Kauffmann.
28. Provost, F., & Fawcett, T. (2001). Robust Classification for Imprecise Environments. Machine Learning, 42/3, 203-231.
29. Provost, F., Fawcett, T., &: Kohavi, R. (1998). The Case Against Accuracy Estimation for Comparing Induction Algorithms. In Proceedings of the Fifteenth International Conference on Machine Learning, pp. 445-453 Madison, WI. Morgan Kauffmann.
30. Quinlan, J. (1992). C4-5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA.
31. Solberg, A., & Solberg, R. (1996). A Large-Scale Evaluation of Features for Automatic Detection of Oil Spills in ERS SAR Images. In International Geoscience and Remote Sensing Symposium, pp. 1484-1486 Lincoln, NE.
32. Stanfill, C., & Waltz, D. (1986). Toward Memory-based Reasoning. Communications of the ACM, 29(12), 213-1228.
33. Swets, J. (1988). Measuring the Accuracy of Diagnostic Systems. Science, 240, 1285-1293.
34. Tomek, I. (1976). Two Modifications of CNN. IEEE Transactions on Systems, Man and Cybernetics, 6, 769-772.
35. Turney, P. (1996). Cost Sensitive Bibliography, http://ai.iit.nrc.ca/ bibiliographies/cost-sensitive.html.
36. van Rijsbergen, C., Harper, D., & Porter, M. (1981). The Selection of Good Search Terms. Information Processing and Management, 17, 77-91.
37. Woods, K., Doss, C., Bowyer, K., Solka, J., Priebe, C., & Kegelmeyer, P. (1993). Comparative Evaluation of Pattern Recognition Techniques for Detection of Microcalcifications in Mammography. International Journal of Pattern Recognition and Artificial Intelligence, 7(6), 1417-1436.