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arXiv · 1904.09235

Reliable Multi-label Classification: Prediction with Partial Abstention

Vu-Linh Nguyen·Eyke Hüllermeier
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Abstract

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.

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Vu-Linh Nguyen, Eyke Hüllermeier. 2019-04-19. Reliable Multi-label Classification: Prediction with Partial Abstention. https://arxiv.org/abs/1904.09235

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