%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 }