Kernel-Based Enhanced Oversampling Method for Imbalanced Classification

Li Wenjie, Hanlin WANG, Sibo Zhu, Zhijian Li

Abstract: This paper introduces a novel oversampling technique designed to improve classification performance on imbalanced datasets. The proposed method, Kernel-Weighted SMOTE (KWSMOTE), enhances the traditional SMOTE algorithm by employing a kernel-based weighting scheme to prioritize closer neighbors, which guides a convex combination that ensures the generated samples are geometrically bounded. This dual-mechanism approach generates synthetic samples that better represent the minority class. Through experiments on multiple real-world datasets, we demonstrate that KWSMOTE outperforms existing methods in terms of F1-score, G-mean, and AUC, providing a robust solution for handling imbalanced datasets in classification tasks.

Submission Number: 199