Entropy-based hybrid sampling (EHS) method to handle class overlap in highly imbalanced dataset

Abstract

Class imbalance and class overlap create difficulties in the training phase of the standard machine learning algorithm. Its performance is not well in minority classes, especially when there is a high class imbalance and significant class overlap. Recently it has been observed by researchers that, the joint effects of class overlap and imbalance are more harmful as compared to their direct impact. To handle these problems, many methods have been proposed by researchers in past years that can be broadly categorized as data-level, algorithm-level, ensemble learning, and hybrid methods. Existing data-level methods often suffer from problems like information loss and overfitting. To overcome these problems, we introduce a novel entropy-based hybrid sampling (EHS) method to handle class overlap in highly imbalanced datasets. The EHS eliminates less informative majority instances from the overlap region during the undersampling phase and regenerates high informative synthetic minority instances in the oversampling phase near the borderline. The proposed EHS achieved significant improvement in F1-score, G-mean, and AUC performance metrics value by DT, NB, and SVM classifiers as compared to well-established state-of-the-art methods. Classifiers performances are tested on 28 datasets with extreme ranges in imbalance and overlap.