Kernel-Based Enhanced Oversampling Method for Imbalanced Classification

• URL: http://arxiv.org/abs/2504.09147v1
• Date: Sat, 12 Apr 2025 09:24:23 GMT
• Title: Kernel-Based Enhanced Oversampling Method for Imbalanced Classification
• Authors: Wenjie Li, Sibo Zhu, Zhijian Li, Hanlin Wang,
• Abstract summary: This paper introduces a novel oversampling technique designed to improve classification performance on imbalanced datasets.<n>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.
• Score: 10.112750055561877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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