Novel Apriori-Based Multi-Label Learning Algorithm by Exploiting Coupled Label Relationship

Wang, Z; Cao, L

Novel Apriori-Based Multi-Label Learning Algorithm by Exploiting Coupled Label Relationship

Wang, Z Cao, L

Permalink

Publication Type:: Journal Article
Citation:: Journal of Beijing Institute of Technology (English Edition), 2017, 26 (2), pp. 206 - 214
Issue Date:: 2017-06-01

Closed Access

	Filename	Description	Size
	Novel Apriori_Ba_Ê¡ÂÔ_bel Relationship_Zhe.pdf	Published Version	406.68 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

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	2017-06-01	en_US
dc.identifier.citation	Journal of Beijing Institute of Technology (English Edition), 2017, 26 (2), pp. 206 - 214	en_US
dc.identifier.issn	1004-0579	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123751
dc.description.abstract	© 2017 Editorial Department of Journal of Beijing Institute of Technology. It is a key challenge to exploit the label coupling relationship in multi-label classification (MLC) problems. Most previous work focused on label pairwise relations, in which generally only global statistical information is used to analyze the coupled label relationship. In this work, firstly Bayesian and hypothesis testing methods are applied to predict the label set size of testing samples within their k nearest neighbor samples, which combines global and local statistical information, and then apriori algorithm is used to mine the label coupling relationship among multiple labels rather than pairwise labels, which can exploit the label coupling relations more accurately and comprehensively. The experimental results on text, biology and audio datasets shown that, compared with the state-of-the-art algorithm, the proposed algorithm can obtain better performance on 5 common criteria.	en_US
dc.relation.ispartof	Journal of Beijing Institute of Technology (English Edition)	en_US
dc.relation.isbasedon	10.15918/j.jbit1004-0579.201726.0209	en_US
dc.subject.classification	General Science & Technology	en_US
dc.title	Novel Apriori-Based Multi-Label Learning Algorithm by Exploiting Coupled Label Relationship	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	26	en_US
utslib.for	0802 Computation Theory and Mathematics	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	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

© 2017 Editorial Department of Journal of Beijing Institute of Technology. It is a key challenge to exploit the label coupling relationship in multi-label classification (MLC) problems. Most previous work focused on label pairwise relations, in which generally only global statistical information is used to analyze the coupled label relationship. In this work, firstly Bayesian and hypothesis testing methods are applied to predict the label set size of testing samples within their k nearest neighbor samples, which combines global and local statistical information, and then apriori algorithm is used to mine the label coupling relationship among multiple labels rather than pairwise labels, which can exploit the label coupling relations more accurately and comprehensively. The experimental results on text, biology and audio datasets shown that, compared with the state-of-the-art algorithm, the proposed algorithm can obtain better performance on 5 common criteria.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/123751