Issue No. 08 - Aug. (2014 vol. 26)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2013.39
Min-Ling Zhang , School of Computer Science and Engineering, Southeast University, Nanjing, China
Zhi-Hua Zhou , National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
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.
learning (artificial intelligence),
M. Zhang and Z. Zhou, "A Review on Multi-Label Learning Algorithms," in IEEE Transactions on Knowledge & Data Engineering, vol. 26, no. 8, pp. 1819-1837, 2014.