Reliable Multi-label Classification: Prediction with Partial Abstention
Reliable Multi-label Classification: Prediction with Partial Abstention
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DOI：
10.1609/aaai.v34i04.5972
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发表时间：
2019-04
期刊：
影响因子：
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通讯作者：
Vu-Linh Nguyen;Eyke Hüllermeier
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文献类型：
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作者：
Vu-Linh Nguyen;Eyke Hüllermeier
与传统的(单标签)分类不同，多标签分类(MLC)的设置允许一个实例同时属于几个类。因此，预测采用所有标签的子集的形式，而不是选择单个类别标签。在本文中，我们研究了MLC设置的一种扩展，其中允许学习者部分放弃预测，即对某些类别标签提供预测，但不一定对所有类别标签进行预测。我们提出了一种基于广义损失最小化问题的MLC的形式化方法，并首次给出了Hamming损失、秩损失和F-测度情况下的理论和实验结果。
In contrast to conventional (single-label) classification, the setting of multilabel classification (MLC) allows an instance to belong to several classes simultaneously. Thus, instead of selecting a single class label, predictions take the form of a subset of all labels. In this paper, we study an extension of the setting of MLC, in which the learner is allowed to partially abstain from a prediction, that is, to deliver predictions on some but not necessarily all class labels. We propose a formalization of MLC with abstention in terms of a generalized loss minimization problem and present first results for the case of the Hamming loss, rank loss, and F-measure, both theoretical and experimental.