Radial-based oversampling based on differential evolution for imbalanced data

Data imbalance remains a significant obstacle in many real-world applications. Although the Synthetic Minority Over-sampling Technique (SMOTE) and its variants are widely used to mitigate this issue, they often suffer from noise sensitivity, over-constraint, and over-generalization. In this paper, we introduce Radial-Based Oversampling based on Differential Evolution (DERBO), a novel algorithm that combines the global search strength of differential evolution (DE) with a radial basis function (RBF)-guided fitness strategy. By generating synthetic samples that are both diverse and closely aligned with the original minority distribution, DERBO effectively overcomes the limitations of existing methods. Extensive comparisons across 32 datasets against nine state-of-the-art imbalanced learning techniques demonstrate DERBO’s consistently superior performance, establishing it as a highly competitive and robust solution for addressing data imbalance.