Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 26, Issue 8, Pages 1819-1837Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2013.39
Keywords
Multi-label learning; label correlations; problem transformation; algorithm adaptation
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Funding
- National Science Foundation of China [61073097, 61175049, 61222309]
- National Fundamental Research Program of China [2010CB327903]
- MOE Program for New Century Excellent Talents in University [NCET-13-0130]
- Fundamental Research Funds for the Central Universities (the Cultivation Program for Young Faculties of Southeast University)
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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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