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 …

challenge class classification cs.lg data detection discrimination economic financial financial fraud fraud fraud detection instances kernel losses samples smote space