Rethinking Oversampling With Class Alliance Constraints From Data Complexity Perspective

Class overlap is a major factor of data complexity that hampers classifier performance, particularly in imbalanced learning scenarios. Most existing oversampling methods rely on conservative seed sample selection and decoupled synthesis strategies, which limit sample diversity and fail to effectively control overlap risk. This paper proposes a novel oversampling framework called TMACO (Class Alliance-Constrained Oversampling), which integrates data complexity considerations into both seed selection and sample generation. First, TMACO selects seed sample units using a class alliance constraint that jointly considers spatial geometry and class distribution to enhance diversity and representativeness. Second, it generates synthetic samples based on three-point units to ensure regional stability. Third, a region-level filtering mechanism is applied to prevent synthetic samples from intruding into majority class areas. Extensive experiments on benchmark and real-world datasets demonstrate that TMACO consistently improves minority class performance and overall classification accuracy compared to state-of-the-art oversampling techniques. The proposed method also offers interpretable parameter control and adapts well to varying task objectives.