Unlike the typical classification setting where each instance is associated with a single class, in multi-label learning each instance is associated with multiple classes simultaneously. Therefore the learning task in this setting is to predict the subset of classes to which each instance belongs. This work examines the application of a recently developed framework called Conformal Prediction (CP) to the multi-label learning setting. CP complements the predictions of machine learning algorithms with reliable measures of confidence. As a result the proposed approach instead of just predicting the most likely subset of classes for a new unseen instance, also indicates the likelihood of each predicted subset being correct. This additional information is especially valuable in the multi-label setting where the overall uncertainty is extremely high.

翻译：与每个实例都与单类相关联的典型分类设置不同,在多标签学习中,每个实例同时与多类相关联。因此,这一设置的学习任务是预测每个实例所属的类别子集。这项工作审查了对多标签学习环境适用最近开发的称为 " 非正式预测 " (CP)的框架的情况。CP以可靠的信任度来补充机器学习算法的预测。因此,拟议的方法而不是仅仅预测新未知实例中最可能的类别子集,还表明每个预测子集的正确可能性。在多标签环境中,这种额外信息特别宝贵,因为那里的总体不确定性极高。