Confronting Discrimination in Classification: Smote Based on Marginalized Minorities in the Kernel Space for Imbalanced Data

Financial fraud detection poses a typical challenge characterized by class imbalance, where instances of fraud are extremely rare but can lead to unpredictable economic losses if misidentified. Precisely classifying these critical minority samples represents a challenging task within the classification. The primary difficulty arises from mainstream classifiers, which often exhibit "implicit discrimination" against minority samples in evaluation metrics, which results in frequent misclassifications, and the key to the problem lies in the overlap of feature spaces between majority and minority samples. To address these challenges, oversampling is a feasible solution, yet current classical oversampling methods often lack the necessary caution in sample selection, exacerbating feature space overlap. In response, we propose a novel classification oversampling approach based on the decision boundary and sample proximity relationships. This method carefully considers the distance between critical samples and the decision hyperplane, as well as the density of surrounding samples, resulting in an adaptive oversampling strategy in the kernel space. Finally, we test the proposed method on a classic financial fraud dataset, and the results show that our proposed method provides an effective and robust solution that can improve the classification accuracy of minorities.