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Experimental Comparison of Different Problem Transformation Methods for Multi-Label Classification using MEKA

by Hiteshri Modi, Mahesh Panchal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 59 - Number 15
Year of Publication: 2012
Authors: Hiteshri Modi, Mahesh Panchal
10.5120/9622-4268

Hiteshri Modi, Mahesh Panchal . Experimental Comparison of Different Problem Transformation Methods for Multi-Label Classification using MEKA. International Journal of Computer Applications. 59, 15 ( December 2012), 10-15. DOI=10.5120/9622-4268

@article{ 10.5120/9622-4268,
author = { Hiteshri Modi, Mahesh Panchal },
title = { Experimental Comparison of Different Problem Transformation Methods for Multi-Label Classification using MEKA },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 59 },
number = { 15 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 10-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume59/number15/9622-4268/ },
doi = { 10.5120/9622-4268 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:04:16.409346+05:30
%A Hiteshri Modi
%A Mahesh Panchal
%T Experimental Comparison of Different Problem Transformation Methods for Multi-Label Classification using MEKA
%J International Journal of Computer Applications
%@ 0975-8887
%V 59
%N 15
%P 10-15
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Classification of multi-label and multi-target data is challenging task for machine learning community. It includes converting the problem in other easily solvable form or extending the existing algorithms to directly cope up with multi-label or multi-target data. There are several approaches in both these category. Since this problem has many applications in image classification, document classification, bio data classification etc. much research is going on in this specific domain. In this paper some experiments are performed on real multi-label datasets and three measures like hamming loss, exact match and accuracy are compared of different problem transformation methods. Finally what is effect of these results on further research is also highlighted.

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Index Terms

Computer Science
Information Sciences

Keywords

Binary Relevance Label Power-Set Label Ranking MEKA Multi-Label Ranking Pruned Set

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