Binary bitwise artificial bee colony as feature selection optimization approach within taguchi's t-method

Nolia, Harudin and Faizir, Ramlie and Wan Zuki Azman, Wan Muhamad and Muhtazaruddin, M. N. and Khairur Rijal, Jamaludin and Mohd Yazid, Abu and Zulkifli Marlah, Marlan (2021) Binary bitwise artificial bee colony as feature selection optimization approach within taguchi's t-method. Mathematical Problems in Engineering, 2021. pp. 1-10. ISSN 1024-123X. (Published)

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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.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Forecasting; Large dataset; Optimization; Search engines
Subjects: T Technology > TJ Mechanical engineering and machinery
T Technology > TS Manufactures
Faculty/Division: Faculty of Manufacturing and Mechatronic Engineering Technology
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 18 Apr 2022 02:12
Last Modified: 18 Apr 2022 02:12
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