Coupled Attribute Similarity Learning on Categorical Data for Multi-Label Classification

Wang, Z; Cao, L

Coupled Attribute Similarity Learning on Categorical Data for Multi-Label Classification

Wang, Z Cao, L

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Publication Type:: Journal Article
Citation:: Journal of Beijing Institute of Technology (English Edition), 2017, 26 (3), pp. 404 - 410
Issue Date:: 2017-09-01

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Field	Value	Language
dc.contributor.author	Wang, Z	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.date.issued	2017-09-01	en_US
dc.identifier.citation	Journal of Beijing Institute of Technology (English Edition), 2017, 26 (3), pp. 404 - 410	en_US
dc.identifier.issn	1004-0579	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125878
dc.description.abstract	© 2017 Editorial Department of Journal of Beijing Institute of Technology . In this paper a novel coupled attribute similarity learning method is proposed with the basis on the multi-label categorical data (CASonMLCD). The CASonMLCD method not only computes the correlations between different attributes and multi-label sets using information gain, which can be regarded as the important degree of each attribute in the attribute learning method, but also further analyzes the intra-coupled and inter-coupled interactions between an attribute value pair for different attributes and multiple labels. The paper compared the CASonMLCD method with the OF distance and Jaccard similarity, which is based on the MLKNN algorithm according to 5 common evaluation criteria. The experiment results demonstrated that the CASonMLCD method can mine the similarity relationship more accurately and comprehensively, it can obtain better performance than compared methods.	en_US
dc.relation.ispartof	Journal of Beijing Institute of Technology (English Edition)	en_US
dc.relation.isbasedon	10.15918/j.jbit1004-0579.201726.0317	en_US
dc.subject.classification	General Science & Technology	en_US
dc.title	Coupled Attribute Similarity Learning on Categorical Data for Multi-Label Classification	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	26	en_US
utslib.for	0199 Other Mathematical Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

© 2017 Editorial Department of Journal of Beijing Institute of Technology . In this paper a novel coupled attribute similarity learning method is proposed with the basis on the multi-label categorical data (CASonMLCD). The CASonMLCD method not only computes the correlations between different attributes and multi-label sets using information gain, which can be regarded as the important degree of each attribute in the attribute learning method, but also further analyzes the intra-coupled and inter-coupled interactions between an attribute value pair for different attributes and multiple labels. The paper compared the CASonMLCD method with the OF distance and Jaccard similarity, which is based on the MLKNN algorithm according to 5 common evaluation criteria. The experiment results demonstrated that the CASonMLCD method can mine the similarity relationship more accurately and comprehensively, it can obtain better performance than compared methods.

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http://hdl.handle.net/10453/125878