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Naive Bayes-Guided Bat Algorithm for Feature Selection
Joint Authors
Taha, Ahmed Majid
Mustapha, Aida
Chen, Soong-Der
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-12-14
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Taha, Ahmed Majid& Mustapha, Aida& Chen, Soong-Der. 2013. Naive Bayes-Guided Bat Algorithm for Feature Selection. The Scientific World Journal،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1032789
Modern Language Association (MLA)
Taha, Ahmed Majid…[et al.]. Naive Bayes-Guided Bat Algorithm for Feature Selection. The Scientific World Journal No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-1032789
American Medical Association (AMA)
Taha, Ahmed Majid& Mustapha, Aida& Chen, Soong-Der. Naive Bayes-Guided Bat Algorithm for Feature Selection. The Scientific World Journal. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1032789
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1032789