A Robust Oversampling Approach for Class Imbalance Problem with Small Disjuncts

Abstract

Class imbalance is one of the important challenges for machine learning because of it's learning to bias toward the majority classes. The oversampling method is a fundamental imbalance-learning technique with many real-world applications. However, when the small disjuncts problem occurs, how to effectively avoiding the negative oversampling results rather than using clusters previously, remains a challenging task. Thus, this study introduces a disjuncts-robust oversampling (DROS) method. The novel method shows that the data filling of new synthetic samples to the minority class areas in data space can be thought of as the searchlight illuminating with light cones to the restricted areas in real life. In the first step, DROS computes a series of light-cone structures that is first started from the inner minority class area, then passes through the boundary minority class area, last is stopped by the majority class area. In the second step, DROS generates new synthetic samples in those light-cone structures. Experiments considering both real-world and 2D emulational datasets demonstrate that our method outperforms the current state-of-the-art oversampling methods and suggest that our method is able to deal with the small disjuncts.