Publication:
Naive Bayes-guided bat algorithm for feature selection

Simple item page
dc.citedby58
dc.contributor.authorTaha A.M.en_US
dc.contributor.authorMustapha A.en_US
dc.contributor.authorChen S.-D.en_US
dc.contributor.authorid55699699200en_US
dc.contributor.authorid57200530694en_US
dc.contributor.authorid7410253413en_US
dc.date.accessioned2023-12-29T07:43:44Z
dc.date.available2023-12-29T07:43:44Z
dc.date.issued2013
dc.description.abstractWhen 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo325973
dc.identifier.doi10.1155/2013/325973
dc.identifier.scopus2-s2.0-84893863229
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84893863229&doi=10.1155%2f2013%2f325973&partnerID=40&md5=12405b4254ba7b7eb95d3ddf53cca428
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29950
dc.identifier.volume2013
dc.relation.ispartofAll Open Access; Gold Open Access; Green Open Access
dc.sourceScopus
dc.sourcetitleThe Scientific World Journal
dc.subjectAlgorithms
dc.subjectAnimals
dc.subjectArtificial Intelligence
dc.subjectBayes Theorem
dc.subjectBiomimetics
dc.subjectChiroptera
dc.subjectEcholocation
dc.subjectPattern Recognition, Automated
dc.subjectalgorithm
dc.subjectanimal
dc.subjectarticle
dc.subjectartificial intelligence
dc.subjectautomated pattern recognition
dc.subjectbat
dc.subjectBayes theorem
dc.subjectbiomimetics
dc.subjectecholocation
dc.subjectmethodology
dc.subjectphysiology
dc.titleNaive Bayes-guided bat algorithm for feature selectionen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS