An Ensemble Imbalanced Classification Framework via Dual-Perspective Overlapping Analysis with Multi-Resolution Metrics

The coexistence of class imbalance and overlap poses a major challenge in classification and significantly limits model accuracy. Data-level methods alleviate class imbalance by generating samples, but without ensuring their rationality, which may introduce noise. Algorithm-level methods are designed based on the model training process, avoiding noise introduction. However, existing methods often fail to consider the potential multiclass scenarios within overlap regions or design targeted solutions for different overlap patterns. This paper proposes an ensemble imbalanced classification framework via dual-perspective overlapping analysis with multi-resolution metrics. The dataset is divided into multiple resolutions for independent analysis, capturing distributional information from local to global levels. For each independent resolution, overlap is analyzed from the perspectives of “feature overlap” and “instance overlap” to derive more refined overlap scores. Flow model mapping and importance weighting are, respectively, applied to refine overlapping samples according to the two criteria. During testing, classifiers are adaptively selected based on the overlap degree of test samples under different criteria, and predictions across resolutions are integrated for the final decision. Experiments on 39 datasets demonstrate that the proposed method outperforms typical imbalanced classification methods in F-measure and G-mean, with particularly notable gains on 15 severely overlapping datasets.