Comparative evaluation of multi-label classification methods

Nasierding, Gulisong and Kouzani, Abbas Z. 2012, Comparative evaluation of multi-label classification methods, in FSKD 2012 : Proceedings of the 9th International Conference on Fuzzy Systems and Knowledge Discovery, IEEE, Piscataway, N.J., pp. 679-683, doi: 10.1109/FSKD.2012.6234347.

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Title Comparative evaluation of multi-label classification methods
Author(s) Nasierding, Gulisong
Kouzani, Abbas Z.ORCID iD for Kouzani, Abbas Z.
Conference name Fuzzy Systems and Knowledge Discovery. Conference (9th : 2012 : Chongqing, China)
Conference location Chongqing, China
Conference dates 29-31 May. 2012
Title of proceedings FSKD 2012 : Proceedings of the 9th International Conference on Fuzzy Systems and Knowledge Discovery
Editor(s) [Unknown]
Publication date 2012
Conference series Fuzzy Systems and Knowledge Discovery Conference
Start page 679
End page 683
Total pages 5
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) algorithm
comparative evaluation
evaluation metrics
multi-label classification
multi-label data
Summary This paper presents a comparative evaluation of popular multi-label classification methods on several multi-label problems from different domains. The methods include multi-label k-nearest neighbor, binary relevance, label power set, random k-label set ensemble learning, calibrated label ranking, hierarchy of multi-label classifiers and triple random ensemble multi-label classification algorithms. These multi-label learning algorithms are evaluated using several widely used MLC evaluation metrics. The evaluation results show that for each multi-label classification problem a particular MLC method can be recommended. The multi-label evaluation datasets used in this study are related to scene images, multimedia video frames, diagnostic medical report, email messages, emotional music data, biological genes and multi-structural proteins categorization.
ISBN 1467300225
Language eng
DOI 10.1109/FSKD.2012.6234347
Field of Research 090609 Signal Processing
Socio Economic Objective 920203 Diagnostic Methods
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2012, IEEE
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