Research Article
Experimental Comparison of Methods for Multi-label Classification in different Application Domains
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 114 - Issue 19 |
| Published: March 2015 |
| Authors: Passent El Kafrawy, Amr Mausad, Heba Esmail |
10.5120/20083-1666
|
Passent El Kafrawy, Amr Mausad, Heba Esmail . Experimental Comparison of Methods for Multi-label Classification in different Application Domains. International Journal of Computer Applications. 114, 19 (March 2015), 1-9. DOI=10.5120/20083-1666
@article{ 10.5120/20083-1666,
author = { Passent El Kafrawy,Amr Mausad,Heba Esmail },
title = { Experimental Comparison of Methods for Multi-label Classification in different Application Domains },
journal = { International Journal of Computer Applications },
year = { 2015 },
volume = { 114 },
number = { 19 },
pages = { 1-9 },
doi = { 10.5120/20083-1666 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2015
%A Passent El Kafrawy
%A Amr Mausad
%A Heba Esmail
%T Experimental Comparison of Methods for Multi-label Classification in different Application Domains%T
%J International Journal of Computer Applications
%V 114
%N 19
%P 1-9
%R 10.5120/20083-1666
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Real-world applications have begun to adopt the multi-label paradigm. The multi-label classification implies an extra dimension because each example might be associated with multiple labels (different possible classes), as opposed to a single class or label (binary, multi-class) classification. And with increasing number of possible multi-label applications in most ecosystems, there is little effort in comparing the different multi-label methods in different domains. Hence, there is need for a comprehensive overview of methods and metrics. In this study, we experimentally evaluate 11 methods for multi-label learning using 6 evaluation measures over seven benchmark datasets. The results of the experimental comparison revealed that the best performing method for both the example- based evaluation measures and the label-based evaluation measures are ECC on all measures when using C4. 5 tree classifier as a single-label base learner.
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