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Naive Bayes-guided bat algorithm for feature selection

When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or...

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Main Authors:	Taha A.M., Mustapha A., Chen S.-D.
Other Authors:	55699699200
Format:	Article
Published:	2023
Subjects:	Algorithms Animals Artificial Intelligence Bayes Theorem Biomimetics Chiroptera Echolocation Pattern Recognition, Automated algorithm animal article artificial intelligence automated pattern recognition bat Bayes theorem biomimetics echolocation methodology physiology
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id	my.uniten.dspace-29950
record_format	dspace
spelling	my.uniten.dspace-299502023-12-29T15:43:44Z Naive Bayes-guided bat algorithm for feature selection Taha A.M. Mustapha A. Chen S.-D. 55699699200 57200530694 7410253413 Algorithms Animals Artificial Intelligence Bayes Theorem Biomimetics Chiroptera Echolocation Pattern Recognition, Automated algorithm animal article artificial intelligence automated pattern recognition bat Bayes theorem biomimetics echolocation methodology physiology When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing. Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. The performance of the proposed feature selection algorithm was investigated using twelve benchmark datasets from different domains and was compared to three other well-known feature selection algorithms. Discussion focused on four perspectives: number of features, classification accuracy, stability, and feature generalization. The results showed that BANB significantly outperformed other algorithms in selecting lower number of features, hence removing irrelevant, redundant, or noisy features while maintaining the classification accuracy. BANB is also proven to be more stable than other methods and is capable of producing more general feature subsets. � 2013 Ahmed Majid Taha et al. Final 2023-12-29T07:43:44Z 2023-12-29T07:43:44Z 2013 Article 10.1155/2013/325973 2-s2.0-84893863229 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893863229&doi=10.1155%2f2013%2f325973&partnerID=40&md5=12405b4254ba7b7eb95d3ddf53cca428 https://irepository.uniten.edu.my/handle/123456789/29950 2013 325973 All Open Access; Gold Open Access; Green Open Access Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Algorithms Animals Artificial Intelligence Bayes Theorem Biomimetics Chiroptera Echolocation Pattern Recognition, Automated algorithm animal article artificial intelligence automated pattern recognition bat Bayes theorem biomimetics echolocation methodology physiology
spellingShingle	Algorithms Animals Artificial Intelligence Bayes Theorem Biomimetics Chiroptera Echolocation Pattern Recognition, Automated algorithm animal article artificial intelligence automated pattern recognition bat Bayes theorem biomimetics echolocation methodology physiology Taha A.M. Mustapha A. Chen S.-D. Naive Bayes-guided bat algorithm for feature selection
description	When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing. Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. The performance of the proposed feature selection algorithm was investigated using twelve benchmark datasets from different domains and was compared to three other well-known feature selection algorithms. Discussion focused on four perspectives: number of features, classification accuracy, stability, and feature generalization. The results showed that BANB significantly outperformed other algorithms in selecting lower number of features, hence removing irrelevant, redundant, or noisy features while maintaining the classification accuracy. BANB is also proven to be more stable than other methods and is capable of producing more general feature subsets. � 2013 Ahmed Majid Taha et al.
author2	55699699200
author_facet	55699699200 Taha A.M. Mustapha A. Chen S.-D.
format	Article
author	Taha A.M. Mustapha A. Chen S.-D.
author_sort	Taha A.M.
title	Naive Bayes-guided bat algorithm for feature selection
title_short	Naive Bayes-guided bat algorithm for feature selection
title_full	Naive Bayes-guided bat algorithm for feature selection
title_fullStr	Naive Bayes-guided bat algorithm for feature selection
title_full_unstemmed	Naive Bayes-guided bat algorithm for feature selection
title_sort	naive bayes-guided bat algorithm for feature selection
publishDate	2023
_version_	1806424544046481408
score	13.222552

Naive Bayes-guided bat algorithm for feature selection

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