Cost-sensitive learning by cost-proportionate example weighting

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2003, Pages 435-442

  Zadrozny, Bianca ^a   Langford, John ^a,b   Abe, Naoki ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

^b TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 33749245586     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Anifantis, S. The DMEF Data Set Library. The Direct Marketing Association, New York, NY, 2002. [http://www.the-dma.org/dmef/dmefdset.shtml]
2. Domingos, P. MetaCost: A general method for making classifiers cost sensitive. Proceedings of the 5th International Conference on Knowledge Discovery and Data Mining, 155-164, 1999.
3. Drummond, C. & Holte, R. Exploiting the cost (in)sensitivity of decision tree splitting criteria. Proceedings of the 17th International Conference on Machine Learning, 239-246, 2000.
4. Ehrenfeucht, A., Haussler, D., Kearns, M. & Valiant. A general lower bound on the number of examples needed for learning. Information and Computation, 82:3, 247-261, 1989.
5. Elkan, C. Boosting and naive bayesian learning (Technical Report). University of California, San Diego, 1997.
6. Elkan, C. The foundations of cost-sensitive learning. Proceedings of the 17th International Joint Conference on Artificial Intelligence, 973-978, 2001.
7. Fan, W., Stolfo, S., Zhang, J. & Chan, P. AdaCost: Misclassification cost-sensitive boosting. Proceedings of the 16th International Conference on Machine Learning, 97-105, 1999.
8. Freund, Y. & Schapire, R. E. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55:1, 119-139, 1997. (Pubitemid 127433398)
9. Hettich, S. & Bay, S. D. The UCI KDD Archive. University of California, Irvine. [http://kdd.ics.uci.edu/].
10. Joachims, T. Making large-scale SVM learning practical. In Advances in Kernel Methods - Support Vector Learning. MIT Press, 1999.
11. Joachims, T. Estimating the generalization performance of a SVM efficiently. Proceedings of the 17th International Conference on Machine Learning, 431-438, 2000.
12. Kearns, M., Schapire, R., & Sellie, L. Toward Efficient Agnostic Learning. Machine Learning, 17, 115-141, 1998.
13. Kearns, M. Efficient noise-tolerant learning from statistical queries. Journal of the ACM, 45:6, 983-1006, 1998.
14. Margineantu, D. Class probability estimation and cost-sensitive classification decisions. Proceedings of the 13th European Conference on Machine Learning, 270-281, 2002.
15. Quinlan, J. R. Boosting, Bagging, and C4.5. Proceedings of the Thirteenth National Conference on Artificial Intelligence, 725-730, 1996.
16. Quinlan, J. R. C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann, 1993.
17. Valiant, L. A theory of the learnable. Communications of the ACM, 27:11, 1134-1142, 1984.
18. von Neumann, J. Various techniques used in connection with random digits, National Bureau of Standards, Applied Mathematics Series, 12, 36-38, 1951.
19. Zadrozny, B. and Elkan, C. Learning and making decisions when costs and probabilities are both unknown. Proceedings of the 7th International Conference on Knowledge Discovery and Data Mining, 203-213, 2001.