A Novel Hybrid-Based Ensemble for Class Imbalance Problem

Abstract

Class-imbalance is very common in real world. However, conventional advanced methods do not work well on imbalanced data due to imbalanced class distribution. This paper proposes a simple but effective Hybrid-based Ensemble (HE) to deal with two-class imbalanced problem. HE learns a hybrid ensemble using the following two stages: (1) learning several projection matrixes from the rebalanced data obtained by under-sampling the original training set and constructing new training sets by projecting the original training set to different spaces defined by the matrixes, and (2) undersampling several subsets from each new training set and training a model on each subset. Here, feature projection aims to improve the diversity between ensemble members and under-sampling technique is to improve generalization ability of individual members on minority class. Experimental results show that, compared with other state-of-the-art methods, HE shows significantly better performance on measures of AUC, G-mean, F-measure and recall.