GDHS: An efficient hybrid sampling method for multi-class imbalanced data classification

Abstract

Multi-class imbalanced data has received increasing attention from the imbalanced learning community. Numerous methods have emerged in recent years, each of them exhibits superiority in certain scenarios. However, these methods usually confronted with the challenge of the complicated correlations among multiple classes, and the intractable data overlapping between classes poses additional difficulties for modeling the interrelations between classes. To this end, this paper proposes an efficient hybrid sampling method called GDHS for multi-class imbalanced data classification. It considers both the learning difficulty and the generalization potential of the data for minority oversampling. Moreover, it also considers the overlapping problems between classes, introducing majority cleaning strategies to enhance the visibility of each class as well as learning performance. To be specific, it proposes three cleaning strategies for handling the class overlapping problem: (1) self-inner class overlapping information based majority cleaning, (2) global overlapping information based majority cleaning and (3) balanced majority cleaning. Numerical experiments over 20 data sets demonstrate the superiority of the proposed method in terms of mGM and MAUC compared with 12 state-of-the-art methods. The implement of the proposed GDHS in programming language Python is available at https://github.com/ytyancp/GDHS.