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Binary Bitwise Artificial Bee Colony as Feature Selection Optimization Approach within Taguchi's T-Method

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dc.citedby3
dc.contributor.authorHarudin N.en_US
dc.contributor.authorRamlie F.en_US
dc.contributor.authorWan Muhamad W.Z.A.en_US
dc.contributor.authorMuhtazaruddin M.N.en_US
dc.contributor.authorJamaludin K.R.en_US
dc.contributor.authorAbu M.Y.en_US
dc.contributor.authorMarlan Z.M.en_US
dc.contributor.authorid56319654100en_US
dc.contributor.authorid55982859700en_US
dc.contributor.authorid55860800560en_US
dc.contributor.authorid55578437800en_US
dc.contributor.authorid26434395500en_US
dc.contributor.authorid55983627200en_US
dc.contributor.authorid57223885180en_US
dc.date.accessioned2023-05-29T09:11:41Z
dc.date.available2023-05-29T09:11:41Z
dc.date.issued2021
dc.descriptionForecasting; Large dataset; Optimization; Search engines; Artificial bee colonies; High dimensionality; Mahalanobis-taguchi systems; Objective functions; Optimization approach; Prediction accuracy; Prediction techniques; Suboptimal solution; Predictive analyticsen_US
dc.description.abstractTaguchi's T-Method is one of the Mahalanobis Taguchi System-(MTS-) ruled prediction techniques that has been established specifically but not limited to small, multivariate sample data. The prediction model's complexity aspect can be further enhanced by removing features that do not provide valuable information on the overall prediction. In order to accomplish this, a matrix called orthogonal array (OA) is used within the existing Taguchi's T-Method. However, OA's fixed-scheme matrix and its drawback in coping with the high-dimensionality factor led to a suboptimal solution. On the contrary, the usage of SNR (dB) as its objective function was a reliable measure. The application of Binary Bitwise Artificial Bee Colony (BitABC) has been adopted as the novel search engine that helps cater to OA's limitation within Taguchi's T-Method. The generalization aspect using bootstrap was a fundamental addition incorporated in this research to control the effect of overfitting in the analysis. The adoption of BitABC has been tested on eight (8) case studies, including large and small sample datasets. The result shows improved predictive accuracy ranging between 13.99% and 32.86% depending on cases. This study proved that incorporating BitABC techniques into Taguchi's T-Method methodology effectively improved its prediction accuracy. � 2021 Nolia Harudin et al.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo5592132
dc.identifier.doi10.1155/2021/5592132
dc.identifier.scopus2-s2.0-85106356112
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85106356112&doi=10.1155%2f2021%2f5592132&partnerID=40&md5=a0cdfd6f3298fa2ab4f4b7febb69b5de
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26536
dc.identifier.volume2021
dc.publisherHindawi Limiteden_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleMathematical Problems in Engineering
dc.titleBinary Bitwise Artificial Bee Colony as Feature Selection Optimization Approach within Taguchi's T-Methoden_US
dc.typeArticleen_US
dspace.entity.typePublication
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