NDLI: A Review on Multi-Label Learning Algorithms

Content Provider	IEEE Xplore Digital Library
Author	Min-Ling Zhang Zhi-Hua Zhou
Copyright Year	1989
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.
Sponsorship	IEEE IEEE Comput. Soc. Tech. Committee on Data Eng IEEE Computer Society
Starting Page	1819
Ending Page	1837
Page Count	19
File Size	2314424
File Format	PDF
ISSN	10414347
Volume Number	26
Issue Number	8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Correlation Supervised learning Semantics Machine learning algorithms Algorithm design and analysis Vectors Data mining Computing Methodologies Artificial Intelligence Learning Information Technology and Systems Database Management Database Applications algorithm adaptation Multi-label learning label correlations problem transformation
Content Type	Text
Resource Type	Article
Subject	Information Systems Computational Theory and Mathematics Computer Science Applications

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