标题： 校准提高了错误标记样本的检测能力 显示英文标题

标题： Calibration improves detection of mislabeled examples

摘要： 误标记数据是一个普遍的问题，这会削弱机器学习系统在实际应用中的性能。 缓解这个问题的有效方法是检测误标记实例并对它们进行特殊处理，例如过滤或重新标记。 自动误标记检测方法通常依赖于训练一个基础机器学习模型，然后对每个实例进行探测以获得一个信任分数，以确定每个提供的标签是否真实或错误。 因此，这个基础模型的特性至关重要。 在本文中，我们研究了校准此模型的影响。 我们的实证结果表明，使用校准方法可以提高误标记实例检测的准确性和鲁棒性，为工业应用提供了一个实用而有效的解决方案。 显示英文摘要

摘要： Mislabeled data is a pervasive issue that undermines the performance of machine learning systems in real-world applications. An effective approach to mitigate this problem is to detect mislabeled instances and subject them to special treatment, such as filtering or relabeling. Automatic mislabeling detection methods typically rely on training a base machine learning model and then probing it for each instance to obtain a trust score that each provided label is genuine or incorrect. The properties of this base model are thus of paramount importance. In this paper, we investigate the impact of calibrating this model. Our empirical results show that using calibration methods improves the accuracy and robustness of mislabeled instance detection, providing a practical and effective solution for industrial applications.