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A Review on Multi-Label Learning Algorithms.
- Source :
-
IEEE Transactions on Knowledge & Data Engineering . Aug2014, Vol. 26 Issue 8, p1819-1837. 19p. - Publication Year :
- 2014
-
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. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 10414347
- Volume :
- 26
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Knowledge & Data Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 97068729
- Full Text :
- https://doi.org/10.1109/TKDE.2013.39