Class imbalance problem using a hybrid ensemble approach

Abstract

This paper proposes a comparative study that investigates the effects of using resampling (undersampling and oversampling) methods with homogenous ensemble methods Bagging and AdaBoost in imbalanced data sets. We presented a hybrid ensemble approach that combined multi resampling by integrating bot h undersampling and oversampling to get benefits and reduces drawbacks caused by each of them. The proposed approach has improved the performance even those most sensitive to imbalanced class data sets.