Kernel-Based Enhanced Oversampling Method for Imbalanced Classification
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.
Cite
Text
Wenjie et al. "Kernel-Based Enhanced Oversampling Method for Imbalanced Classification." Proceedings of the 17th Asian Conference on Machine Learning, 2025.Markdown
[Wenjie et al. "Kernel-Based Enhanced Oversampling Method for Imbalanced Classification." Proceedings of the 17th Asian Conference on Machine Learning, 2025.](https://mlanthology.org/acml/2025/wenjie2025acml-kernelbased/)BibTeX
@inproceedings{wenjie2025acml-kernelbased,
title = {{Kernel-Based Enhanced Oversampling Method for Imbalanced Classification}},
author = {Wenjie, Li and Wang, Hanlin and Zhu, Sibo and Li, Zhijian},
booktitle = {Proceedings of the 17th Asian Conference on Machine Learning},
year = {2025},
pages = {862-877},
volume = {304},
url = {https://mlanthology.org/acml/2025/wenjie2025acml-kernelbased/}
}