DMCA
A Review on Multi-Label Learning Algorithms
| Citations: | 41 - 7 self |
BibTeX
@MISC{Zhang_areview,
author = {Min-ling Zhang and Zhi-hua Zhou},
title = { A Review on Multi-Label Learning Algorithms},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
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 towards 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.
Keyphrases
multi-label learning algorithm significant amount relevant analysis evaluation metric multi-label learning timely review common notation learning setting open research problem reference purpose representative multi-label learning algorithm state-of-the-art multi-label learning algorithm multi-label learning study single instance online resource formal definition past decade
