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arXiv · 2504.09147

Kernel-Based Enhanced Oversampling Method for Imbalanced Classification

Wenjie Li·Sibo Zhu·Zhijian Li·Hanlin Wang
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Abstract

This paper introduces a novel oversampling technique designed to improve classification performance on imbalanced datasets. The proposed method enhances the traditional SMOTE algorithm by incorporating convex combination and kernel-based weighting to generate synthetic samples that better represent the minority class. Through experiments on multiple real-world datasets, we demonstrate that the new technique outperforms existing methods in terms of F1-score, G-mean, and AUC, providing a robust solution for handling imbalanced datasets in classification tasks.

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Wenjie Li, Sibo Zhu, Zhijian Li, Hanlin Wang. 2025-04-12. Kernel-Based Enhanced Oversampling Method for Imbalanced Classification. https://arxiv.org/abs/2504.09147

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