A new combination sampling method for imbalanced data

Abstract

Imbalanced data is commonly in the real world and brings a lot of challenges. In this paper, we propose a combination sampling method which resamples both minority class and majority class. Improved SMOTE (ISMOTE) is used to do over-sampling on minority class, while distance-based under-sampling (DUS) method is used to do under-sampling on majority class. We adjust the sampling times to search for the optimal results while maintain the dataset size unchanged. Experiments on UCI datasets show that the proposed method performs better than using single over-sampling or under-sampling method. © 2013 Springer-Verlag.