A Review on Multi-Label Learning Algorithms

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 t...

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Bibliographic Details
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
Online Access:Get full text
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Summary: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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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2013.39