Binary bitwise artificial bee colony as feature selection optimization approach within Taguchi’s T-method
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 valuabl...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Hindawi Publishing Corporation
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/31846/1/Binary%20bitwise%20artificial%20bee%20colony%20as%20feature%20selection.pdf |
| _version_ | 1825813935488499712 |
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| author | Nolia, Harudin Faizir, Ramlie Wan Zuki Azman, Wan Muhamad M. N., Muhtazaruddin Khairur Rijal, Jamaludin Mohd Yazid, Abu Zulkifli Marlah, Marlan |
| author_facet | Nolia, Harudin Faizir, Ramlie Wan Zuki Azman, Wan Muhamad M. N., Muhtazaruddin Khairur Rijal, Jamaludin Mohd Yazid, Abu Zulkifli Marlah, Marlan |
| author_sort | Nolia, Harudin |
| collection | UMP |
| description | 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. |
| first_indexed | 2024-03-06T12:51:23Z |
| format | Article |
| id | UMPir31846 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:51:23Z |
| publishDate | 2021 |
| publisher | Hindawi Publishing Corporation |
| record_format | dspace |
| spelling | UMPir318462021-09-14T07:39:08Z http://umpir.ump.edu.my/id/eprint/31846/ Binary bitwise artificial bee colony as feature selection optimization approach within Taguchi’s T-method Nolia, Harudin Faizir, Ramlie Wan Zuki Azman, Wan Muhamad M. N., Muhtazaruddin Khairur Rijal, Jamaludin Mohd Yazid, Abu Zulkifli Marlah, Marlan TS Manufactures 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. Hindawi Publishing Corporation 2021-05-07 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/31846/1/Binary%20bitwise%20artificial%20bee%20colony%20as%20feature%20selection.pdf Nolia, Harudin and Faizir, Ramlie and Wan Zuki Azman, Wan Muhamad and M. N., Muhtazaruddin 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 (5592132). pp. 1-10. ISSN 1024-123X (print); 1563-5147 (online). (Published) https://doi.org/10.1155/2021/5592132 https://doi.org/10.1155/2021/5592132 |
| spellingShingle | TS Manufactures Nolia, Harudin Faizir, Ramlie Wan Zuki Azman, Wan Muhamad M. N., Muhtazaruddin Khairur Rijal, Jamaludin Mohd Yazid, Abu Zulkifli Marlah, Marlan Binary bitwise artificial bee colony as feature selection optimization approach within Taguchi’s T-method |
| title | 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_short | 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 |
| topic | TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/31846/1/Binary%20bitwise%20artificial%20bee%20colony%20as%20feature%20selection.pdf |
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