  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.
