Feature subset selection using differential evolution

Khushaba, RN; Al-Ani, A; Al-Jumaily, A

Feature subset selection using differential evolution

Khushaba, RN

Al-Ani, A

Al-Jumaily, A

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Publication Type:: Chapter
Citation:: 2009, 5506 LNCS pp. 103 - 110
Issue Date:: 2009-09-21

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Field	Value	Language
dc.contributor.author	Khushaba, RN 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.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2009-09-21	en_US
dc.identifier.citation	2009, 5506 LNCS pp. 103 - 110	en_US
dc.identifier.isbn	3642024890	en_US
dc.identifier.isbn	9783642024894	en_US
dc.identifier.uri	http://hdl.handle.net/10453/7833
dc.description.abstract	One of the fundamental motivations for feature selection is to overcome the curse of dimensionality. A novel feature selection algorithm is developed in this chapter based on a combination of Differential Evolution (DE) optimization technique and statistical feature distribution measures. The new algorithm, referred to as DEFS, utilizes the DE float number optimizer in a combinatorial optimization problem like feature selection. The proposed DEFS highly reduces the computational cost while at the same time proves to present a powerful performance. The DEFS is tested as a search procedure on different datasets with varying dimensionality. Practical results indicate the significance of the proposed DEFS in terms of solutions optimality and memory requirements. © 2009 Springer Berlin Heidelberg.	en_US
dc.relation.isbasedon	10.1007/978-3-642-02490-0_13	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Feature subset selection using differential evolution	en_US
dc.type	Chapter
utslib.citation.volume	5506 LNCS	en_US
utslib.for	090399 Biomedical Engineering not elsewhere classified	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	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/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	5506 LNCS	en_US

Abstract:

One of the fundamental motivations for feature selection is to overcome the curse of dimensionality. A novel feature selection algorithm is developed in this chapter based on a combination of Differential Evolution (DE) optimization technique and statistical feature distribution measures. The new algorithm, referred to as DEFS, utilizes the DE float number optimizer in a combinatorial optimization problem like feature selection. The proposed DEFS highly reduces the computational cost while at the same time proves to present a powerful performance. The DEFS is tested as a search procedure on different datasets with varying dimensionality. Practical results indicate the significance of the proposed DEFS in terms of solutions optimality and memory requirements. © 2009 Springer Berlin Heidelberg.

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http://hdl.handle.net/10453/7833