EasyEnsemble and BalanceCascade are two class-imbalance learning methods. They can adaptively exploit the majority class examples, avoiding important majority class examples to be ignored by common under-sampling while maintaining the fast training speed of under-sampling.Reference
X.-Y. Liu, J. Wu, and Z.-H. Zhou. Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man and Cybernetics - Part B: Cybernetics, 2009, 39(2): 539-550. (early version at ICDM'06)
This package is free for academic usage. You can run it at your own risk. For other purposes, please contact Prof. Zhi-Hua Zhou (email@example.com).Requirement
This package is developed with Matlab R2008b. Statistics Toolbox (version 7.0) is also required. Its implementation of Classfication and Regression Trees (‘treefit’ function) is used as base learner of AdaBoost.
This package was developed by Ms. Xu-Ying Liu (firstname.lastname@example.org) and Mr. Jianxin Wu (email@example.com). For any problem concerning the code, please feel free to contact Ms. Xu-Ying Liu (firstname.lastname@example.org).Download