A Hybrid Sampling Approach Integrating Decision Tree and Subregional SMOTE for Imbalanced Credit Classification With Class Overlapping

Class imbalance and class overlapping are two prevalent data traits that collectively impair the generalisation performance of credit classification models. For this superposition trait, conventional sampling techniques or remedial measures either amplify marginal noise or irreversibly discard useful majority information. To address these issues, a hybrid sampling method integrating a decision tree (DT) and a subregional synthetic minority oversampling technique (SR‐SMOTE) algorithm is proposed. On the one hand, a decision tree‐based undersampling (DTU) algorithm is proposed, which innovatively embeds classifier training into the sampling process to iteratively identify and eliminate hard‐to‐classify samples from the majority class. This can effectively retain the key information of the majority class and alleviate the problem of data imbalance. On the other hand, an adaptive SR‐SMOTE algorithm is developed, which partitions the minority class boundary into subregions based on local class overlap density and performs weighted oversampling within each subregion. This focuses on the most ambiguous areas and mitigates the risk of generating overlapping synthetic samples, thereby reducing inter‐class overlap in imbalanced data. The proposed method is validated on four imbalanced credit datasets. Experimental results show that it outperforms benchmark models. Robustness tests with GBDT, KNN, and LogR confirm consistent performance gains, with GBDT achieving the best results. This suggests the proposed approach is effective for credit classification with imbalanced and overlapping data.