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<p>This research introduces a novel deep learning based oversampling method for the class imbalance problem. Compared to previous methods, our approach defines the oversampling process as a composition of multiple decisions. This allows the deep learning classifier to learn the optimal mechanism for each decision from the ground truth data patterns, enabling more fine-grained control over the data oversampling process. We provide experiments on real-world datasets to demonstrate the superiority of our solution over the state-of-the-art oversampling methods.</p>

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Deep learning meets oversampling: a learning framework to handle imbalanced classification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月19日

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  • Sukumar Kishanthan,
  • Asela Hevapathige

摘要

This research introduces a novel deep learning based oversampling method for the class imbalance problem. Compared to previous methods, our approach defines the oversampling process as a composition of multiple decisions. This allows the deep learning classifier to learn the optimal mechanism for each decision from the ground truth data patterns, enabling more fine-grained control over the data oversampling process. We provide experiments on real-world datasets to demonstrate the superiority of our solution over the state-of-the-art oversampling methods.