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Feature selection for high dimensional data: An evolutionary filter approach.

Problem statement: Feature selection is a task of crucial importance for the application of machine learning in various domains. In addition, the recent increase of data dimensionality poses a severe challenge to many existing feature selection approaches with respect to efficiency and effectiveness...

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Main Authors: Yahya, Anwar Ali, Osman, Addin, Ramli, Abdul Rahman, Balola, Adlan
Format: Article
Language:English
English
Published: Science Publications 2011
Online Access:http://psasir.upm.edu.my/id/eprint/23508/1/Feature%20selection%20for%20high%20dimensional%20data.pdf
http://psasir.upm.edu.my/id/eprint/23508/
http://ww.scipub.org/‎
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spelling my.upm.eprints.235082015-10-28T03:35:09Z http://psasir.upm.edu.my/id/eprint/23508/ Feature selection for high dimensional data: An evolutionary filter approach. Yahya, Anwar Ali Osman, Addin Ramli, Abdul Rahman Balola, Adlan Problem statement: Feature selection is a task of crucial importance for the application of machine learning in various domains. In addition, the recent increase of data dimensionality poses a severe challenge to many existing feature selection approaches with respect to efficiency and effectiveness. As an example, genetic algorithm is an effective search algorithm that lends itself directly to feature selection; however this direct application is hindered by the recent increase of data dimensionality. Therefore adapting genetic algorithm to cope with the high dimensionality of the data becomes increasingly appealing. Approach: In this study, we proposed an adapted version of genetic algorithm that can be applied for feature selection in high dimensional data. The proposed approach is based essentially on a variable length representation scheme and a set of modified and proposed genetic operators. To assess the effectiveness of the proposed approach, we applied it for cues phrase selection and compared its performance with a number of ranking approaches which are always applied for this task. Results and Conclusion: The results provide experimental evidences on the effectiveness of the proposed approach for feature selection in high dimensional data. Science Publications 2011 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/23508/1/Feature%20selection%20for%20high%20dimensional%20data.pdf Yahya, Anwar Ali and Osman, Addin and Ramli, Abdul Rahman and Balola, Adlan (2011) Feature selection for high dimensional data: An evolutionary filter approach. Journal of Computer Science, 7 (5). pp. 800-820. ISSN 1549-3636 http://ww.scipub.org/‎ 10.3844/jcssp.2011.800.820 English
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
English
description Problem statement: Feature selection is a task of crucial importance for the application of machine learning in various domains. In addition, the recent increase of data dimensionality poses a severe challenge to many existing feature selection approaches with respect to efficiency and effectiveness. As an example, genetic algorithm is an effective search algorithm that lends itself directly to feature selection; however this direct application is hindered by the recent increase of data dimensionality. Therefore adapting genetic algorithm to cope with the high dimensionality of the data becomes increasingly appealing. Approach: In this study, we proposed an adapted version of genetic algorithm that can be applied for feature selection in high dimensional data. The proposed approach is based essentially on a variable length representation scheme and a set of modified and proposed genetic operators. To assess the effectiveness of the proposed approach, we applied it for cues phrase selection and compared its performance with a number of ranking approaches which are always applied for this task. Results and Conclusion: The results provide experimental evidences on the effectiveness of the proposed approach for feature selection in high dimensional data.
format Article
author Yahya, Anwar Ali
Osman, Addin
Ramli, Abdul Rahman
Balola, Adlan
spellingShingle Yahya, Anwar Ali
Osman, Addin
Ramli, Abdul Rahman
Balola, Adlan
Feature selection for high dimensional data: An evolutionary filter approach.
author_facet Yahya, Anwar Ali
Osman, Addin
Ramli, Abdul Rahman
Balola, Adlan
author_sort Yahya, Anwar Ali
title Feature selection for high dimensional data: An evolutionary filter approach.
title_short Feature selection for high dimensional data: An evolutionary filter approach.
title_full Feature selection for high dimensional data: An evolutionary filter approach.
title_fullStr Feature selection for high dimensional data: An evolutionary filter approach.
title_full_unstemmed Feature selection for high dimensional data: An evolutionary filter approach.
title_sort feature selection for high dimensional data: an evolutionary filter approach.
publisher Science Publications
publishDate 2011
url http://psasir.upm.edu.my/id/eprint/23508/1/Feature%20selection%20for%20high%20dimensional%20data.pdf
http://psasir.upm.edu.my/id/eprint/23508/
http://ww.scipub.org/‎
_version_ 1643828079619997696
score 13.223943

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