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OBKA-FS: an oppositional-based binary kidney-inspired search algorithm for feature selection

Feature selection is a key step when building an automatic classification system. Numerous evolutionary algorithms applied to remove irrelevant features in order to make the classifier perform more accurate. Kidney-inspired search algorithm (KA) is a very modern evolutionary algorithm. The original...

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Main Authors:	Taqi, M. K., Ali, R.
Format:	Article
Language:	English
Published:	Asian Research Publishing Network 2017
Subjects:	QH Natural history
Online Access:	http://eprints.utm.my/id/eprint/76660/1/MustafaKadhimTaqi2017_OBKA-FSanOppositionalbasedBinary.pdf http://eprints.utm.my/id/eprint/76660/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010206562&partnerID=40&md5=08d1e3a9cad45315a61ffdd7328e0a3a
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spelling	my.utm.766602018-04-30T13:48:22Z http://eprints.utm.my/id/eprint/76660/ OBKA-FS: an oppositional-based binary kidney-inspired search algorithm for feature selection Taqi, M. K. Ali, R. QH Natural history Feature selection is a key step when building an automatic classification system. Numerous evolutionary algorithms applied to remove irrelevant features in order to make the classifier perform more accurate. Kidney-inspired search algorithm (KA) is a very modern evolutionary algorithm. The original version of KA performed more effectively compared with other evolutionary algorithms. However, KA was proposed for continuous search spaces. For feature subset selection and many optimization problems such as classification, binary discrete space is required. Moreover, the movement operator of solutions is notably affected by its own best-known solution found up to now, denoted as Sbest. This may be inadequate if Sbest is located near a local optimum as it will direct the search process to a suboptimal solution. In this study, a three-fold improvement in the existing KA is proposed. First, a binary version of the kidney-inspired algorithm (BKA-FS) for feature subset selection is introduced to improve classification accuracy in multi-class classification problems. Second, the proposed BKA-FS is integrated into an oppositional-based initialization method in order to start with good initial solutions. Thus, this improved algorithm denoted as OBKA-FS. Third, a novel movement strategy based on the calculation of mutual information (MI), which gives OBKA-FS the ability to work in a discrete binary environment has been proposed. For evaluation, an experiment was conducted using ten UCI machine learning benchmark instances. Results show that OBKA-FS outperforms the existing state-of-the-art evolutionary algorithms for feature selection. In particular, OBKA-FS obtained better accuracy with same or fewer features and higher dependency with less redundancy. Thus, the results confirm the high performance of the improved kidney-inspired algorithm in solving optimization problems such as feature selection. Asian Research Publishing Network 2017 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/76660/1/MustafaKadhimTaqi2017_OBKA-FSanOppositionalbasedBinary.pdf Taqi, M. K. and Ali, R. (2017) OBKA-FS: an oppositional-based binary kidney-inspired search algorithm for feature selection. Journal of Theoretical and Applied Information Technology, 95 (1). pp. 9-23. ISSN 1992-8645 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010206562&partnerID=40&md5=08d1e3a9cad45315a61ffdd7328e0a3a
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	QH Natural history
spellingShingle	QH Natural history Taqi, M. K. Ali, R. OBKA-FS: an oppositional-based binary kidney-inspired search algorithm for feature selection
description	Feature selection is a key step when building an automatic classification system. Numerous evolutionary algorithms applied to remove irrelevant features in order to make the classifier perform more accurate. Kidney-inspired search algorithm (KA) is a very modern evolutionary algorithm. The original version of KA performed more effectively compared with other evolutionary algorithms. However, KA was proposed for continuous search spaces. For feature subset selection and many optimization problems such as classification, binary discrete space is required. Moreover, the movement operator of solutions is notably affected by its own best-known solution found up to now, denoted as Sbest. This may be inadequate if Sbest is located near a local optimum as it will direct the search process to a suboptimal solution. In this study, a three-fold improvement in the existing KA is proposed. First, a binary version of the kidney-inspired algorithm (BKA-FS) for feature subset selection is introduced to improve classification accuracy in multi-class classification problems. Second, the proposed BKA-FS is integrated into an oppositional-based initialization method in order to start with good initial solutions. Thus, this improved algorithm denoted as OBKA-FS. Third, a novel movement strategy based on the calculation of mutual information (MI), which gives OBKA-FS the ability to work in a discrete binary environment has been proposed. For evaluation, an experiment was conducted using ten UCI machine learning benchmark instances. Results show that OBKA-FS outperforms the existing state-of-the-art evolutionary algorithms for feature selection. In particular, OBKA-FS obtained better accuracy with same or fewer features and higher dependency with less redundancy. Thus, the results confirm the high performance of the improved kidney-inspired algorithm in solving optimization problems such as feature selection.
format	Article
author	Taqi, M. K. Ali, R.
author_facet	Taqi, M. K. Ali, R.
author_sort	Taqi, M. K.
title	OBKA-FS: an oppositional-based binary kidney-inspired search algorithm for feature selection
title_short	OBKA-FS: an oppositional-based binary kidney-inspired search algorithm for feature selection
title_full	OBKA-FS: an oppositional-based binary kidney-inspired search algorithm for feature selection
title_fullStr	OBKA-FS: an oppositional-based binary kidney-inspired search algorithm for feature selection
title_full_unstemmed	OBKA-FS: an oppositional-based binary kidney-inspired search algorithm for feature selection
title_sort	obka-fs: an oppositional-based binary kidney-inspired search algorithm for feature selection
publisher	Asian Research Publishing Network
publishDate	2017
url	http://eprints.utm.my/id/eprint/76660/1/MustafaKadhimTaqi2017_OBKA-FSanOppositionalbasedBinary.pdf http://eprints.utm.my/id/eprint/76660/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010206562&partnerID=40&md5=08d1e3a9cad45315a61ffdd7328e0a3a
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score	13.211869

OBKA-FS: an oppositional-based binary kidney-inspired search algorithm for feature selection

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