A Review on Multi-Label Learning Algorithms

• Published in: IEEE transactions on knowledge and data engineering Vol. 26; no. 8; pp. 1819 - 1837
• Main Authors: Zhang, Min-Ling, Zhou, Zhi-Hua
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Artificial Intelligence; Computing Methodologies; Correlation; Data mining; Database Applications; Database Management; Information Technology and Systems; Learning; Machine learning algorithms; Semantics; Supervised learning; Training; Vectors; Multi-label learning; problem transformation; algorithm adaptation; label correlations
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2013.39

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.