A dataset is imbalanced if the classification categories are not approximately equally represented. Data sampling techniques attempt to alleviate the problem of class imbalance by adjusting the class distribution of the training data set. This can be accomplished by either removing examples from the majority class (undersampling) or adding examples to the minority class (oversampling).
In this repository, SMOTEBoost, RB-Boost, and RUS-Boost as three approaches to building ensembles of classifiers for two-class imbalanced data sets have been implemented and their performance measures and ROC curves on 5-fold imbalanced yeast dataset have been reported and their results have been compared with SVM classifier and AdaBoost-M2 and RandomForest ensembles.
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[5] Seiffert, Chris, et al. "RUSBoost: A hybrid approach to alleviating class imbalance." IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 40.1 (2009): 185-197.