标题： 通过虚假相关性的视角进行不平衡分类 显示英文标题

标题： Imbalanced Classification through the Lens of Spurious Correlations

摘要： 类不平衡是机器学习中的一个基本挑战，经常导致不可靠的分类性能。 虽然以前的方法集中在数据或损失重新加权方案上，但我们认为不平衡是一种数据条件，通过少数类的不足定义放大了聪明汉斯（CH）效应。 在基于反事实解释的方法中，我们提出利用可解释的人工智能来共同识别和消除在不平衡下出现的CH效应。 我们的方法在三个数据集上实现了具有竞争力的分类性能，并展示了CH效应如何在不平衡下出现，这是一种现有方法大多忽视的视角。 显示英文摘要

摘要： Class imbalance poses a fundamental challenge in machine learning, frequently leading to unreliable classification performance. While prior methods focus on data- or loss-reweighting schemes, we view imbalance as a data condition that amplifies Clever Hans (CH) effects by underspecification of minority classes. In a counterfactual explanations-based approach, we propose to leverage Explainable AI to jointly identify and eliminate CH effects emerging under imbalance. Our method achieves competitive classification performance on three datasets and demonstrates how CH effects emerge under imbalance, a perspective largely overlooked by existing approaches.