1Paolo S. A multi-objective optimization approach for class imbalance learning[J]. Pattern Recogni- tion, 2011, 44(8): 801-1810.
2Japkowicz N, Stephen S. The class imbalance problem: asystematic study[J]. Intelligent Data Anal- ysis Journal, 2002, 6(5): 429-450.
3Weiss G M. Ming with Rarity: A Unifying Framework[J]. SIGKDD Explorations, 2004, 6(1): 7-19.
4Gustavo E, Batista P, Ronaldo C. A study of the behavior of several methods for balancing machine learning training data[J]. Sigkdd Explorations, 2004, 6(1): 20-29.
5. Chawla N V, Bowyer K W, Hall L O. SMOTE: synthetie minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2002, 6(16): 321-357.
6Methan M, Agrawal R, Rissanen J. SLI.Q: A fast scalable classifier for data mining[J]. Lecture Notes in Computer Sci.Proc.of the 5th Int. conf.on Extending Database Tech, 1996: 18-33.
7Han H, Wang W Y, Mao B H. Borderline-SMOTE:Anew over-sampling method in imbalanced data sets learning[C]//Proc of International Conference on Intelligent Computing(ICIC'05), 2005: 878-887.
9Breiman L. Random Forests[J]. Machine Learning, 2001, 45(1): 5-32.
10Yen S-J, Lee Y-S. Cluster-based under-sampling approaches for imbalanced data distributions[J]. Expert Systems with Applications, 2009, 36(3): 5718-5727.