重新思考机器学习中的类别不平衡
Rethinking Class Imbalance in Machine Learning
AI总结：
本文提出更广的类别不平衡分类法，涵盖五种类型和全局/局部层次，理论分析其影响，并基于logit扰动提出新损失函数，元学习调参，实验验证有效性。
摘要：
不平衡学习是机器学习的一个子领域，专注于在类别不平衡的情况下进行学习任务。几乎所有现有研究都将类别不平衡视为比例不平衡，即每个类别中训练样本的比例不平衡。忽视比例不平衡将导致类别之间或类别内部的不公平以及较差的泛化能力。以往文献提出了众多方法，用于不平衡学习的理论/实证分析或新方法。本研究提出了一种更广泛视角下的机器学习中类别不平衡的新分类法。总结了可能存在于机器学习任务中的其他四种不平衡类型，即类别之间的方差、距离、邻域和质量不平衡。还提出了包括全局和局部在内的两个不同层次的不平衡。理论分析用于说明新不平衡类型对学习公平性的显著影响。此外，我们的分类法和理论结论被用于分析几种经典方法的缺点。作为示例，当比例、方差和距离不平衡同时存在时，我们提出了一种新的基于logit扰动的不平衡学习损失。几种经典损失成为我们提出方法的特例。利用元学习来推断与这三种不平衡类型相关的超参数。在多个基准语料库上的实验结果验证了所提出方法的有效性。
英文摘要：
Imbalance learning is a subfield of machine learning that focuses on learning tasks in the presence of class imbalance. Nearly all existing studies refer to class imbalance as a proportion imbalance, where the proportion of training samples in each class is not balanced. The ignorance of the proportion imbalance will result in unfairness between/among classes and poor generalization capability. Previous literature has presented numerous methods for either theoretical/empirical analysis or new methods for imbalance learning. This study presents a new taxonomy of class imbalance in machine learning with a broader scope. Four other types of imbalance, namely, variance, distance, neighborhood, and quality imbalances between/among classes, which may exist in machine learning tasks, are summarized. Two different levels of imbalance including global and local are also presented. Theoretical analysis is used to illustrate the significant impact of the new imbalance types on learning fairness. Moreover, our taxonomy and theoretical conclusions are used to analyze the shortcomings of several classical methods. As an example, we propose a new logit perturbation-based imbalance learning loss when proportion, variance, and distance imbalances exist simultaneously. Several classical losses become the special case of our proposed method. Meta learning is utilized to infer the hyper-parameters related to the three types of imbalance. Experimental results on several benchmark corpora validate the effectiveness of the proposed method.
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