Logo Logo
Help
Contact
Switch Language to German

Nguyen, Vu-Linh ORCID logoORCID: https://orcid.org/0000-0003-1642-4468 and Hüllermeier, Eyke ORCID logoORCID: https://orcid.org/0000-0002-9944-4108 (February 2020): Reliable Multilabel Classification: Prediction with Partial Abstention. 34th AAAI Conference on Artificial Intelligence, New York, USA, 7-12 Feburary 2020. Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34, No. 04 pp. 5264-5271

Full text not available from 'Open Access LMU'.

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.

Actions (login required)

View Item View Item