Feature subset selection using differential evolution and a statistical repair mechanism

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

Feature subset selection using differential evolution and a statistical repair mechanism

Khushaba, RN

Al-Ani, A

Al-Jumaily, A

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Publication Type:: Journal Article
Citation:: Expert Systems with Applications, 2011, 38 (9), pp. 11515 - 11526
Issue Date:: 2011-09-01

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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	2011-09-01	en_US
dc.identifier.citation	Expert Systems with Applications, 2011, 38 (9), pp. 11515 - 11526	en_US
dc.identifier.issn	0957-4174	en_US
dc.identifier.uri	http://hdl.handle.net/10453/14543
dc.description.abstract	One of the fundamental motivations for feature selection is to overcome the curse of dimensionality problem. This paper presents a novel feature selection method utilizing a combination of differential evolution (DE) optimization method and a proposed repair mechanism based on feature distribution measures. The new method, abbreviated as DEFS, utilizes the DE float number optimizer in the combinatorial optimization problem of feature selection. In order to make the solutions generated by the float-optimizer suitable for feature selection, a roulette wheel structure is constructed and supplied with the probabilities of features distribution. These probabilities are constructed during iterations by identifying the features that contribute to the most promising solutions. The proposed DEFS is used to search for optimal subsets of features in datasets with varying dimensionality. It is then utilized to aid in the selection of Wavelet Packet Transform (WPT) best basis for classification problems, thus acting as a part of a feature extraction process. Practical results indicate the significance of the proposed method in comparison with other feature selection methods. © 2011 Elsevier Ltd. All rights reserved.	en_US
dc.relation.ispartof	Expert Systems with Applications	en_US
dc.relation.isbasedon	10.1016/j.eswa.2011.03.028	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Feature subset selection using differential evolution and a statistical repair mechanism	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.citation.volume	38	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
dc.location.activity	Barcelona, Catalonia, 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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	38	en_US

Abstract:

One of the fundamental motivations for feature selection is to overcome the curse of dimensionality problem. This paper presents a novel feature selection method utilizing a combination of differential evolution (DE) optimization method and a proposed repair mechanism based on feature distribution measures. The new method, abbreviated as DEFS, utilizes the DE float number optimizer in the combinatorial optimization problem of feature selection. In order to make the solutions generated by the float-optimizer suitable for feature selection, a roulette wheel structure is constructed and supplied with the probabilities of features distribution. These probabilities are constructed during iterations by identifying the features that contribute to the most promising solutions. The proposed DEFS is used to search for optimal subsets of features in datasets with varying dimensionality. It is then utilized to aid in the selection of Wavelet Packet Transform (WPT) best basis for classification problems, thus acting as a part of a feature extraction process. Practical results indicate the significance of the proposed method in comparison with other feature selection methods. © 2011 Elsevier Ltd. All rights reserved.

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http://hdl.handle.net/10453/14543