Optimizing the k-NN metric weights using differential evolution

AlSukker, A; Khushaba, R; Al-Ani, A

Optimizing the k-NN metric weights using differential evolution

AlSukker, A Khushaba, R

Al-Ani, A

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Publication Type:: Conference Proceeding
Citation:: MCIT'2010: International Conference on Multimedia Computing and Information Technology, 2010, pp. 89 - 92
Issue Date:: 2010-06-01

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Field	Value	Language
dc.contributor.author	AlSukker, A	en_US
dc.contributor.author	Khushaba, R https://orcid.org/0000-0001-8528-8979	en_US
dc.contributor.author	Al-Ani, A https://orcid.org/0000-0002-8092-8954	en_US
dc.date.issued	2010-06-01	en_US
dc.identifier.citation	MCIT'2010: International Conference on Multimedia Computing and Information Technology, 2010, pp. 89 - 92	en_US
dc.identifier.isbn	9781424470037	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16254
dc.description.abstract	Traditional k-NN classifier poses many limitations including that it does not take into account each class distribution, importance of each feature, contribution of each neighbor, and the number ofinstances for each class. A Differential evolution (DE) optimization technique is utilized to enhance the performance of k-NN through optimizing the metric weights of features, neighbors and classes. Several datasets are used to evaluate the performance of the proposed DE based metrics and to compare it to some k-NN variants from the literature. Practical experiments indicate that in most cases, incorporating DE in k-NN classification can provide more accurate performance. ©2010 IEEE.	en_US
dc.relation.ispartof	MCIT'2010: International Conference on Multimedia Computing and Information Technology	en_US
dc.relation.isbasedon	10.1109/MCIT.2010.5444845	en_US
dc.title	Optimizing the k-NN metric weights using differential evolution	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	en_US
dc.location.activity	U.A.E.	en_US
dc.location.activity	ISI:000274867000010
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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

Traditional k-NN classifier poses many limitations including that it does not take into account each class distribution, importance of each feature, contribution of each neighbor, and the number ofinstances for each class. A Differential evolution (DE) optimization technique is utilized to enhance the performance of k-NN through optimizing the metric weights of features, neighbors and classes. Several datasets are used to evaluate the performance of the proposed DE based metrics and to compare it to some k-NN variants from the literature. Practical experiments indicate that in most cases, incorporating DE in k-NN classification can provide more accurate performance. ©2010 IEEE.

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http://hdl.handle.net/10453/16254