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A Review on Multi-Label Learning Algorithms

Zhang, Min-Ling, Zhou, Zhi-Hua·2014·IEEE Transactions on Knowledge and Data Engineering
DOI 10.1109/tkde.2013.39
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Abstract

Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of progresses have been made toward this emerging machine learning paradigm. This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label learning algorithms. Firstly, fundamentals on multi-label learning including formal definition and evaluation metrics are given. Secondly and primarily, eight representative multi-label learning algorithms are scrutinized under common notations with relevant analyses and discussions. Thirdly, several related learning settings are briefly summarized. As a conclusion, online resources and open research problems on multi-label learning are outlined for reference purposes.

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Metadata

Authors
Zhang, M.-L., & Zhou, Z.-H.
Venue
IEEE Transactions on Knowledge and Data Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2014-08
Volume
26
Issue
8
Pages
1819-1837
ISSN
1041-4347 (ISSN-L)
DOI
10.1109/tkde.2013.39
OpenAlex
W2114315281
Cited by
3,321
References
6
FWCI
126.63
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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