Binary Bitwise Artificial Bee Colony as Feature Selection Optimization Approach within Taguchi's T-Method
Forecasting; Large dataset; Optimization; Search engines; Artificial bee colonies; High dimensionality; Mahalanobis-taguchi systems; Objective functions; Optimization approach; Prediction accuracy; Prediction techniques; Suboptimal solution; Predictive analytics
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2023
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my.uniten.dspace-265362023-05-29T17:11:41Z Binary Bitwise Artificial Bee Colony as Feature Selection Optimization Approach within Taguchi's T-Method Harudin N. Ramlie F. Wan Muhamad W.Z.A. Muhtazaruddin M.N. Jamaludin K.R. Abu M.Y. Marlan Z.M. 56319654100 55982859700 55860800560 55578437800 26434395500 55983627200 57223885180 Forecasting; Large dataset; Optimization; Search engines; Artificial bee colonies; High dimensionality; Mahalanobis-taguchi systems; Objective functions; Optimization approach; Prediction accuracy; Prediction techniques; Suboptimal solution; Predictive analytics Taguchi'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. Final 2023-05-29T09:11:41Z 2023-05-29T09:11:41Z 2021 Article 10.1155/2021/5592132 2-s2.0-85106356112 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106356112&doi=10.1155%2f2021%2f5592132&partnerID=40&md5=a0cdfd6f3298fa2ab4f4b7febb69b5de https://irepository.uniten.edu.my/handle/123456789/26536 2021 5592132 All Open Access, Gold, Green Hindawi Limited Scopus |
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Forecasting; Large dataset; Optimization; Search engines; Artificial bee colonies; High dimensionality; Mahalanobis-taguchi systems; Objective functions; Optimization approach; Prediction accuracy; Prediction techniques; Suboptimal solution; Predictive analytics |
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56319654100 |
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56319654100 Harudin N. Ramlie F. Wan Muhamad W.Z.A. Muhtazaruddin M.N. Jamaludin K.R. Abu M.Y. Marlan Z.M. |
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Harudin N. Ramlie F. Wan Muhamad W.Z.A. Muhtazaruddin M.N. Jamaludin K.R. Abu M.Y. Marlan Z.M. |
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Harudin N. Ramlie F. Wan Muhamad W.Z.A. Muhtazaruddin M.N. Jamaludin K.R. Abu M.Y. Marlan Z.M. Binary Bitwise Artificial Bee Colony as Feature Selection Optimization Approach within Taguchi's T-Method |
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Harudin N. |
| title |
Binary Bitwise Artificial Bee Colony as Feature Selection Optimization Approach within Taguchi's T-Method |
| title_short |
Binary Bitwise Artificial Bee Colony as Feature Selection Optimization Approach within Taguchi's T-Method |
| title_full |
Binary Bitwise Artificial Bee Colony as Feature Selection Optimization Approach within Taguchi's T-Method |
| title_fullStr |
Binary Bitwise Artificial Bee Colony as Feature Selection Optimization Approach within Taguchi's T-Method |
| title_full_unstemmed |
Binary Bitwise Artificial Bee Colony as Feature Selection Optimization Approach within Taguchi's T-Method |
| title_sort |
binary bitwise artificial bee colony as feature selection optimization approach within taguchi's t-method |
| publisher |
Hindawi Limited |
| publishDate |
2023 |
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1806426303062081536 |
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13.214268 |