An integrated pruning criterion for ensemble learning based on classification accuracy and diversity

Fu, B; Wang, Z; Pan, R; Xu, G; Dolog, P

An integrated pruning criterion for ensemble learning based on classification accuracy and diversity

Fu, B Wang, Z Pan, R Xu, G

Dolog, P

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Publication Type:: Conference Proceeding
Citation:: Advances in Intelligent Systems and Computing, 2013, 172 AISC pp. 47 - 58
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Fu, B	en_US
dc.contributor.author	Wang, Z	en_US
dc.contributor.author	Pan, R	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.contributor.author	Dolog, P	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Advances in Intelligent Systems and Computing, 2013, 172 AISC pp. 47 - 58	en_US
dc.identifier.isbn	9783642308666	en_US
dc.identifier.issn	2194-5357	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32039
dc.description.abstract	Ensemble pruning is an important issue in the field of ensemble learning. Diversity is a key criterion to determine how the pruning process has been done and measure what result has been derived. However, there is few formal definitions of diversity yet. Hence, three important factors that should be further considered while designing a pruning criterion is presented, and then an effective definition of diversity is proposed. The experimental results have validated that the given pruning criterion could single out the subset of classifiers that show better performance in the process of hill-climbing search, compared with other definitions of diversity and other criteria. © 2013 Springer-Verlag.	en_US
dc.relation.ispartof	Advances in Intelligent Systems and Computing	en_US
dc.relation.isbasedon	10.1007/978-3-642-30867-3_5	en_US
dc.title	An integrated pruning criterion for ensemble learning based on classification accuracy and diversity	en_US
dc.type	Conference Proceeding
utslib.citation.volume	172 AISC	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Salamanca, Spain
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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	172 AISC	en_US

Abstract:

Ensemble pruning is an important issue in the field of ensemble learning. Diversity is a key criterion to determine how the pruning process has been done and measure what result has been derived. However, there is few formal definitions of diversity yet. Hence, three important factors that should be further considered while designing a pruning criterion is presented, and then an effective definition of diversity is proposed. The experimental results have validated that the given pruning criterion could single out the subset of classifiers that show better performance in the process of hill-climbing search, compared with other definitions of diversity and other criteria. © 2013 Springer-Verlag.

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http://hdl.handle.net/10453/32039