Binary particle swarm optimization for variables selection optimization in Taguchi’s T-Method
Prediction analysis has drawn significant interest in numerous fields Taguchi’s T-Method is a prediction tool that was practically developed to predict even with a limited sample data. It was developed explicitly for multivariate system prediction by relying on historical data as the baseline model...
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| Format: | Article |
| Language: | English |
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Penerbit UTM Press
2020
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| Online Access: | http://eprints.utm.my/85556/1/CheMuniraCheRazali2020_BinaryParticleSwarmOptimizationforVariables.pdf |
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| author | Harudin, N. Jamaludin, K. R. Ramlie, F. Muhtazaruddin, M. N. Che Razali, Che Munira Muhamad, W. Z. A. W. |
| author_facet | Harudin, N. Jamaludin, K. R. Ramlie, F. Muhtazaruddin, M. N. Che Razali, Che Munira Muhamad, W. Z. A. W. |
| author_sort | Harudin, N. |
| collection | ePrints |
| description | Prediction analysis has drawn significant interest in numerous fields Taguchi’s T-Method is a prediction tool that was practically developed to predict even with a limited sample data. It was developed explicitly for multivariate system prediction by relying on historical data as the baseline model and adapting the signal to noise ratio (SNR) as well as zero proportional concepts in strengthening its robustness. Orthogonal array (OA) in T-Method is a variable selection optimization technique in improving the prediction accuracy as well as helping to eliminate variables that may deteriorate the overall performance. However, the limitation of OA in dealing with higher multivariate data restrains the optimization accuracy. The binary particle swarm optimization used in this study helps to cater to the limitation of OA as well as optimizing the variable selection process for better prediction accuracy. A comparison between the T-Method+OA and T- Method+BPSO in four different case studies shows that the T-Method+BPSO performs better with a higher coefficient of determination (R2) value and means relative error (MRE) value compared to the T-Method+OA. The T-Method with the BPSO element as variables screening optimization is able to increase or even maintain the prediction accuracy for cases that are normally distributed, have a high R2 value, and with low sample data. |
| first_indexed | 2024-03-05T20:36:16Z |
| format | Article |
| id | utm.eprints-85556 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:36:16Z |
| publishDate | 2020 |
| publisher | Penerbit UTM Press |
| record_format | dspace |
| spelling | utm.eprints-855562020-06-30T08:50:31Z http://eprints.utm.my/85556/ Binary particle swarm optimization for variables selection optimization in Taguchi’s T-Method Harudin, N. Jamaludin, K. R. Ramlie, F. Muhtazaruddin, M. N. Che Razali, Che Munira Muhamad, W. Z. A. W. QA Mathematics Prediction analysis has drawn significant interest in numerous fields Taguchi’s T-Method is a prediction tool that was practically developed to predict even with a limited sample data. It was developed explicitly for multivariate system prediction by relying on historical data as the baseline model and adapting the signal to noise ratio (SNR) as well as zero proportional concepts in strengthening its robustness. Orthogonal array (OA) in T-Method is a variable selection optimization technique in improving the prediction accuracy as well as helping to eliminate variables that may deteriorate the overall performance. However, the limitation of OA in dealing with higher multivariate data restrains the optimization accuracy. The binary particle swarm optimization used in this study helps to cater to the limitation of OA as well as optimizing the variable selection process for better prediction accuracy. A comparison between the T-Method+OA and T- Method+BPSO in four different case studies shows that the T-Method+BPSO performs better with a higher coefficient of determination (R2) value and means relative error (MRE) value compared to the T-Method+OA. The T-Method with the BPSO element as variables screening optimization is able to increase or even maintain the prediction accuracy for cases that are normally distributed, have a high R2 value, and with low sample data. Penerbit UTM Press 2020 Article PeerReviewed application/pdf en http://eprints.utm.my/85556/1/CheMuniraCheRazali2020_BinaryParticleSwarmOptimizationforVariables.pdf Harudin, N. and Jamaludin, K. R. and Ramlie, F. and Muhtazaruddin, M. N. and Che Razali, Che Munira and Muhamad, W. Z. A. W. (2020) Binary particle swarm optimization for variables selection optimization in Taguchi’s T-Method. MATEMATIKA, 36 (1). pp. 69-84. ISSN 0127-9602 https://dx.doi.org/10.11113/matematika.v36.n1.1181 DOI:10.11113/matematika.v36.n1.1181 |
| spellingShingle | QA Mathematics Harudin, N. Jamaludin, K. R. Ramlie, F. Muhtazaruddin, M. N. Che Razali, Che Munira Muhamad, W. Z. A. W. Binary particle swarm optimization for variables selection optimization in Taguchi’s T-Method |
| title | Binary particle swarm optimization for variables selection optimization in Taguchi’s T-Method |
| title_full | Binary particle swarm optimization for variables selection optimization in Taguchi’s T-Method |
| title_fullStr | Binary particle swarm optimization for variables selection optimization in Taguchi’s T-Method |
| title_full_unstemmed | Binary particle swarm optimization for variables selection optimization in Taguchi’s T-Method |
| title_short | Binary particle swarm optimization for variables selection optimization in Taguchi’s T-Method |
| title_sort | binary particle swarm optimization for variables selection optimization in taguchi s t method |
| topic | QA Mathematics |
| url | http://eprints.utm.my/85556/1/CheMuniraCheRazali2020_BinaryParticleSwarmOptimizationforVariables.pdf |
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