机器学习中合成少数群体超采样技术的挑战和局限性

I. Alkhawaldeh, Ibrahem Albalkhi, Abdulqadir J Naswhan
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引用次数: 0

摘要

过度取样是处理阶级不平衡数据集的最常用方法,这一点从过去二十年中开发的大量过度取样方法中可见一斑。在下面的社论中,我们将论证超采样的问题,这些问题源于过度拟合的可能性以及生成的合成案例可能无法准确代表少数群体。在使用超采样技术时应考虑到这些局限性。我们还提出了几种处理不平衡数据的替代策略以及未来工作展望。
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Challenges and limitations of synthetic minority oversampling techniques in machine learning
Oversampling is the most utilized approach to deal with class-imbalanced datasets, as seen by the plethora of oversampling methods developed in the last two decades. We argue in the following editorial the issues with oversampling that stem from the possibility of overfitting and the generation of synthetic cases that might not accurately represent the minority class. These limitations should be considered when using oversampling techniques. We also propose several alternate strategies for dealing with imbalanced data, as well as a future work perspective.
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