1. WU G, CHANG E. Class-boundary alignment for imbalanced data [A]. Workshop on learning from imbanlanced data sets II, ICML[C]. Washington DC: AAAI Press, 2003:49–56.
2. Cristianini N, Shawe Taylor J. An introduction to support vector machines and other kernel based learning methods [M]. Cambridge: Cambridge University Press, 2000.
3. WEISS G M. Learning with rare cases and small disjuncts[C] // Proceedings of the 12th International Conference on Machine Learning. San Francisco: Morgan Kaufinann, 1995:558–565.
4. WEISS G M, H IRSH H. A quantitative study of small disjuncts[C] // Proceedings of the 17th National Conference on Artificial Intelligence. Texas: AAA I Press, 2000: 665–670.
5. DRUMMOND C, HOLTE R. Explicitly representing expected cost: an alternative to ROC representation [C] //Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2000: 187–207.