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, Ahmed Majid, Mustapha, Aida, Chen, Soong Der
格式: Article
语言:English
English
出版: Hindawi Publishing Corporation 2013
在线阅读:http://psasir.upm.edu.my/id/eprint/30624/1/Naive%20bayes.pdf
http://psasir.upm.edu.my/id/eprint/30624/
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spelling my.upm.eprints.306242015-12-29T04:47:47Z http://psasir.upm.edu.my/id/eprint/30624/ Naive bayes-guided bat algorithm for feature selection. Taha, Ahmed Majid Mustapha, Aida Chen, Soong Der 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. Hindawi Publishing Corporation 2013 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/30624/1/Naive%20bayes.pdf Taha, Ahmed Majid and Mustapha, Aida and Chen, Soong Der (2013) Naive bayes-guided bat algorithm for feature selection. The Scientific World Journal, 2013 (325973). pp. 1-9. ISSN 1537-744X 10.1155/2013/325973 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 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.
format Article
author Taha, Ahmed Majid
Mustapha, Aida
Chen, Soong Der
spellingShingle Taha, Ahmed Majid
Mustapha, Aida
Chen, Soong Der
Naive bayes-guided bat algorithm for feature selection.
author_facet Taha, Ahmed Majid
Mustapha, Aida
Chen, Soong Der
author_sort Taha, Ahmed Majid
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.
publisher Hindawi Publishing Corporation
publishDate 2013
url http://psasir.upm.edu.my/id/eprint/30624/1/Naive%20bayes.pdf
http://psasir.upm.edu.my/id/eprint/30624/
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score 13.252575