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.
Description
IEEE Xplore Abstract (Abstract) - A Review On Multi-Label Learning Algorithms
%0 Journal Article
%1 Zhang2013
%A Zhang, M.
%A Zhou, Z.
%D 2013
%J Knowledge and Data Engineering, IEEE Transactions on
%K classification text
%N 99
%P 1
%R 10.1109/TKDE.2013.39
%T A Review On Multi-Label Learning Algorithms
%U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6471714&abstractAccess=no&userType=inst
%V PP
%X 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.
@article{Zhang2013,
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.},
added-at = {2014-06-27T15:50:57.000+0200},
author = {Zhang, M. and Zhou, Z.},
biburl = {https://www.bibsonomy.org/bibtex/2662b85adb4aa22291ec40e75a747fef8/lopusz_kdd},
description = {IEEE Xplore Abstract (Abstract) - A Review On Multi-Label Learning Algorithms},
doi = {10.1109/TKDE.2013.39},
interhash = {9004ef5367a4955fca353353d782cd44},
intrahash = {662b85adb4aa22291ec40e75a747fef8},
issn = {1041-4347},
journal = {Knowledge and Data Engineering, IEEE Transactions on},
keywords = {classification text},
number = 99,
pages = 1,
timestamp = {2014-06-27T15:55:15.000+0200},
title = {A Review On Multi-Label Learning Algorithms},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6471714&abstractAccess=no&userType=inst},
volume = {PP},
year = 2013
}