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Evidential Multi-Label Classification Approach to Learning from Data with Imprecise Labels

机译：带有不精确标签的数据学习的证据多标签分类方法

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摘要

Multi-label classification problems arise in many real-world applications. Classically, in order to construct a multi-label classifier, we assume the existence of a labeled training set, where each instance is associated with a set of labels, and the task is to output a label set for each unseen instance. However, it is not always possible to have perfectly labeled data. In many problems, there is no ground truth for assigning unambiguously a label set to each instance, and several experts have to be consulted. Due to conflicts and lack of knowledge, labels might be wrongly assigned to some instances. This paper describes an evidence formalism suitable to study multi-label classification problems where the training datasets are imperfectly labelled. Several applications demonstrate the efficiency of our apporach.
机译：在许多实际应用中会出现多标签分类问题。经典地，为了构造多标签分类器，我们假设存在一个标签训练集，其中每个实例与一组标签相关联，并且任务是为每个看不见的实例输出一个标签集。但是，并非总是可能具有完全标记的数据。在许多问题中，没有明确地为每个实例分配标签集的事实，必须咨询一些专家。由于冲突和知识不足，标签可能会错误地分配给某些实例。本文描述了一种证据形式主义，适用于研究训练数据集标签不完善的多标签分类问题。几个应用程序证明了我们方法的效率。

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