Reliable Multilabel Classification: Prediction with Partial Abstention

Nguyen, Vu-Linh und Hüllermeier, Eyke (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 Palo Alto, California USA: AAAI Press. pp. 5264-5271

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.

• Item Type: Conference or Workshop Item (Paper)
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 000 Computer science, knowledge, and systems
• ISBN: 978-1-57735-835-0
• ISSN: 2159-5399
• Place of Publication: Palo Alto, California USA
• Language: English