Pairwise Learning for Imbalanced Data Classification
Pairwise Learning for Imbalanced Data Classification
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DOI：
10.1109/csci54926.2021.00102
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发表时间：
2021-12
期刊：
影响因子：
--
通讯作者：
Shu Liu;Qiangian Wu
中科院分区：
文献类型：
--
作者：
Shu Liu;Qiangian Wu
不平衡数据分类问题在实际应用中非常常见，这对传统的分类方法提出了巨大的挑战。传统的分类方法只适用于平衡数据，但当数据不平衡时，通常对少数类的分类效果不佳。通过对少数类进行过采样或对多数类进行低采样来进行重采样预处理有助于提高性能，但可能会受到过拟合或信息丢失的影响。为了克服不平衡数据分类的困难，本文提出了一种新的方法--成对稳健支持向量机。它使非凸稳健支持向量分类损失适应于两两学习环境。在训练过程中，来自少数类和多数类的样本总是成对出现。这会自动平衡两个类别的影响。仿真和实际应用表明，该方法具有很高的效率。
Imbalanced data classification problems appear quite commonly in real-world applications and impose great challenges to traditional classification approaches which work well only on balanced data but usually perform poorly on the minority class when the data is imbalanced. Resampling preprocessing by oversampling the minority class or downsampling the majority class helps improve the performance but may suffer from overfitting or loss of information. In this paper we propose a novel method called pairwise robust support vector machine (PRSVM) to overcome the difficulty of imbalanced data classification. It adapts the non-convex robust support vector classification loss to the pairwise learning setting. In the training process, samples from the minority class and the majority class always appear as pairs. This automatically balances the impact of two classes. Simulations and real-world applications show that PRSVM is highly effective.