Kernel-Based Enhanced Oversampling Method for Imbalanced Classification
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909578152640512 |
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| author | Li, Wenjie Zhu, Sibo Li, Zhijian Wang, Hanlin |
| author_facet | Li, Wenjie Zhu, Sibo Li, Zhijian Wang, Hanlin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_09147 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Kernel-Based Enhanced Oversampling Method for Imbalanced Classification Li, Wenjie Zhu, Sibo Li, Zhijian Wang, Hanlin Machine Learning 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. |
| title | Kernel-Based Enhanced Oversampling Method for Imbalanced Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.09147 |