Dual Self-Paced SMOTE for Imbalanced Data

Abstract

Imbalanced classification has always been a challenging issue. The minority class usually has degraded recognition rate. The key factors are sample scarcity of the minority class and intrinsic complex distribution characteristics in imbalanced data. SMOTE and its extensions are commonly used data-level methods. These SMOTE-related methods oversample all or unsafe minority class examples, without considering sample difference or only considering sample difference in spatial distribution. In this paper, we propose a dual self-paced SMOTE (DSP-SMOTE) method, which considers temporal-spatial distribution of samples. DSP-SMOTE adopts a two-way self-paced mechanism at different stages of sampling. The majority class is undersampled according to easy example first principle, whereas the minority class is oversampled following difficult sample first principle. In DSP-SMOTE, a hybrid (including static and dynamic) difficulty is introduced to estimate easy or difficult examples. Furthermore, the trade-off between static and dynamic difficulties is adjusted adaptively according to sample distribution and training status. Experimental results demonstrate that DSP-SMOTE outperforms previous SMOTE-related methods significantly in terms of AUC and sensitivity.