Multi Label Classification: An Overview
G. Tsoumakas, and I. Katakis.
International Journal of Data Warehouse and Mining 3 (3): 1--13 (2007)

Nowadays, multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization and semantic scene classification. This paper introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative experimental results of certain multi-label classification methods. It also contributes the definition of concepts for the quantification of the multi-label nature of a data set.

search on

This publication has not been reviewed yet.

rating distribution

average user rating0.0 out of 5.0 based on 0 reviews

Please log in to take part in the discussion (add own reviews or comments).

@article{citeulike:2146554,
  abstract = {Nowadays, multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization and semantic scene classification. This paper introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative experimental results of certain multi-label classification methods. It also contributes the definition of concepts for the quantification of the multi-label nature of a data set.},
  added-at = {2009-03-23T21:50:27.000+0100},
  author = {Tsoumakas, G. and Katakis, I.},
  biburl = {https://puma.uni-kassel.de/bibtex/252c3b18481f5146e4c213d609c1143fc/hotho},
  citeulike-article-id = {2146554},
  description = {CiteULike: Multi Label Classification: An Overview},
  editor = {Taniar, David},
  interhash = {f8e6c4b6b3df7461d070a1a9cc1d15c1},
  intrahash = {52c3b18481f5146e4c213d609c1143fc},
  journal = {International Journal of Data Warehouse and Mining},
  keywords = {classification learning machine ml multi_label survey},
  number = 3,
  pages = {1--13},
  posted-at = {2007-12-19 13:38:29},
  priority = {2},
  publisher = {Idea Group Publishing},
  timestamp = {2009-03-23T21:50:27.000+0100},
  title = {Multi Label Classification: An Overview},
  volume = 3,
  year = 2007
}

%0 Journal Article
%1 citeulike:2146554
%A Tsoumakas, G.
%A Katakis, I.
%D 2007
%E Taniar, David
%I Idea Group Publishing
%J International Journal of Data Warehouse and Mining
%K classification learning machine ml multi_label survey
%N 3
%P 1--13
%T Multi Label Classification: An Overview
%V 3
%X Nowadays, multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization and semantic scene classification. This paper introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative experimental results of certain multi-label classification methods. It also contributes the definition of concepts for the quantification of the multi-label nature of a data set.

PUMA

Multi Label Classification: An Overview
G. Tsoumakas, and I. Katakis.
International Journal of Data Warehouse and Mining 3 (3): 1--13 (2007)

Tags

Users

Comments and Reviews

Cite this publication

PUMA

Multi Label Classification: An OverviewG. Tsoumakas, and I. Katakis. International Journal of Data Warehouse and Mining 3 (3): 1--13 (2007)

Tags

Users

Comments and Reviews

Cite this publication

Multi Label Classification: An Overview
G. Tsoumakas, and I. Katakis.
International Journal of Data Warehouse and Mining 3 (3): 1--13 (2007)