Experimental perspectives on learning from imbalanced data

ACM International Conference Proceeding Series

Volumn 227, Issue , 2007, Pages 935-942

  Van Hulse, Jason ^a   Khoshgoftaar, Taghi M ^a   Napolitano, Amri ^a  

^a FLORIDA ATLANTIC UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS; OPTIMIZATION; SAMPLING;

CLASSIFIER PERFORMANCE; IMBALANCED DATA;

UNSUPERVISED LEARNING;

EID: 34547995826     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1273496.1273614     Document Type: Conference Paper

References (18)

1. Barandela, R., Valdovinos, R. M., Sanchez, J. S., & Ferri, F. J. (2004). The unbalanced training sample problem: Under or over sampling? In Joint IAPR International Workshops on Structural, Syntactic, and Statistical Pattern Recognition (SSPR/SPR'04), Lecture Notes in Computer Science 3138, 806-814.
2. Berenson, M. L., Levine, D. M., & Goldstein, M. (1983). Intermediate statistical methods and applications: A computer package approach. Prentice-Hall, Inc.
3. 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.
4. Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32.
5. Chawla, N. V., Hall, L. O., Bowyer, K. W., & Kegelmeyer, W. P. (2002). Smote: Synthetic minority oversampling technique. Journal of Artificial Intelligence. Research, 321-357.
6. Drummond, C., & Holte, R. C. (2003). C4.5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling. Workshop on Learning from Imbalanced Data Sets II, International Conference on Machine Learning.
7. Han, H., Wang, W. Y., & Mao, B. H. (2005). Borderline-smote: A new over-sampling method in imbalanced data sets learning. In International Conference on Intelligent Computing (ICIC'05). Lecture Notes in Computer Science 3644 (pp. 878-887). Springer-Verlag.
8. Hand, D. J. (2005). Good practice in retail credit score-card assessment. Journal of the Operational Research Society, 56, 1109-1117.
9. Japkowicz, N. (2000). Learning from unbalanced data sets: a comparison of various strategies. AAAI Workshop on Learning from Imbalanced Data Sets (AAAI'00) (pp. 10-15).
10. Jo, T., & Japkowicz, N. (2004). Class imbalances versus small disjuncts. SIGKDD Explorations, 6, 40-49.
11. Kubat, M., & Matwin, S. (1997). Addressing the curse of imbalanced training sets: One sided selection. Proceedings of the Fourteenth International Conference on Machine Learning (pp. 179-186). Morgan Kaufmann.
12. Maloof, M. (2003). Learning when data sets are imbalanced and when costs are unequal and unknown. Proceedings of the. ICML'03 Workshop on Learning from Imbalanced Data Sets.
13. Monard, M. C., & Batista, G. E. A. P. A. (2002). Learning with skewed class distributions. Advances in Logic, Artificial Intelligence and Robotics (LAPTEC'02) (pp. 173-180).
14. Quinlan, J. R. (1993). C4-5: Programs for machine learning. San Mateo, California: Morgan Kaufmann.
15. SAS Institute (2004). SAS/STAT user's guide. SAS Institute Inc.
16. Weiss, G. M., & Provost, F. (2003). Learning when training data are costly: the effect of class distribution on tree induction. Journal of Artificial Intelligence Research, 315-354.
17. Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques. San Francisco, California: Morgan Kaufmann. 2nd edition.
18. Wohlin, C., Runeson, P., Host, M., Ohlsson, M. C., Regnell, B., & Wesslen, A. (2000). Experimentation in software engineering: An introduction. Kluwer International Series in Software Engineering. Boston, MA: Kluwer Academic Publishers.