In multi-label classification, each example in a dataset may be annotated as belonging to one or more classes (or none of the classes). Example applications include image (or document) tagging where each possible tag either applies to a particular image (or document) or not. With many possible classes to consider, data annotators are likely to make errors when labeling such data in practice. Here we consider algorithms for finding mislabeled examples in multi-label classification datasets. We propose an extension of the Confident Learning framework to this setting, as well as a label quality score that ranks examples with label errors much higher than those which are correctly labeled. Both approaches can utilize any trained classifier. After demonstrating that our methodology empirically outperforms other algorithms for label error detection, we apply our approach to discover many label errors in the CelebA image tagging dataset.

翻译：在多标签分类中,数据集中的每个示例都可能被标记为属于一个或一个以上类别(或没有一个类别),例如应用程序包括图像(或文件)标记,如果每个可能的标签适用于特定图像(或文件)或不适用于特定图像(或文件),则每个可能的标签都贴上标记。在很多可能的分类中,数据标记员在实践中标签这些数据时可能会出错。在这里,我们考虑在多标签分类数据集中查找错误标签示例的算法。我们提议将 " 保密学习 " 框架扩展至此设置,并将标签质量评分排在标签错误比正确标签错误高得多的标签上。两种方法都可以使用任何经过培训的分类师。在证明我们的方法在实验性地优于其他算法来检测标签错误后,我们运用了我们的方法来发现CelebA图像标记数据集中的许多标签错误。