Multi-oversampling with Evidence Fusion for Imbalanced Data Classification

Oversampling methods concentrate on creating a balanced dataset by generating samples, widely utilized in classifying imbalanced data. However, current oversampling methods overlook the uncertainty in the samples produced, potentially shifting the data’s distribution and adversely affecting the classification outcomes. To address this problem, we introduce a multi-oversampling with evidence fusion (MOEF) method for imbalanced data classification based on Dempster-Shafer theory. We first design a multi-oversampling strategy to produce various balanced datasets, characterizing the uncertainty of generated samples. Then, we develop a discounting fusion rule based on the inconsistency of data distribution post-oversampling, thereby mitigating the adverse effects of data distribution alterations on classification. Extensive testing on various imbalanced datasets indicates that the proposed MOEF method exhibits more satisfactory performance than other related methods.