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Binary particle swarm optimization for variables selection optimization Taguchi's T method

Prediction analysis has drawn signi?cant interest in numerous ?elds 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 an...

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Main Authors: Harudin, N., Jamaludin, K. R., Ramlie, F., Muhtazaruddin, M. N., Che Razali, C. M., W. Muhamad, W. Z. A.
Format: Article
Language:English
Published: Penerbit UTM Press 2020
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/93320/1/NoliaHarudin2020_BinaryParticleSwarmOptimization.pdf
http://eprints.utm.my/id/eprint/93320/
http://dx.doi.org/10.11113/matematika.v36.n1.1181
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spelling my.utm.933202021-11-19T03:15:42Z http://eprints.utm.my/id/eprint/93320/ Binary particle swarm optimization for variables selection optimization Taguchi's T method Harudin, N. Jamaludin, K. R. Ramlie, F. Muhtazaruddin, M. N. Che Razali, C. M. W. Muhamad, W. Z. A. T Technology (General) Prediction analysis has drawn signi?cant interest in numerous ?elds 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 di?erent case studies shows that the T-Method+BPSO performs better with a higher coe?cient 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 R2value, and with low sample data. Penerbit UTM Press 2020 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/93320/1/NoliaHarudin2020_BinaryParticleSwarmOptimization.pdf Harudin, N. and Jamaludin, K. R. and Ramlie, F. and Muhtazaruddin, M. N. and Che Razali, C. M. and W. Muhamad, W. Z. A. (2020) Binary particle swarm optimization for variables selection optimization Taguchi's T method. Matematika . pp. 69-84. ISSN 0127-9602 http://dx.doi.org/10.11113/matematika.v36.n1.1181 DOI: 10.11113/matematika.v36.n1.1181
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Harudin, N.
Jamaludin, K. R.
Ramlie, F.
Muhtazaruddin, M. N.
Che Razali, C. M.
W. Muhamad, W. Z. A.
Binary particle swarm optimization for variables selection optimization Taguchi's T method
description Prediction analysis has drawn signi?cant interest in numerous ?elds 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 di?erent case studies shows that the T-Method+BPSO performs better with a higher coe?cient 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 R2value, and with low sample data.
format Article
author Harudin, N.
Jamaludin, K. R.
Ramlie, F.
Muhtazaruddin, M. N.
Che Razali, C. M.
W. Muhamad, W. Z. A.
author_facet Harudin, N.
Jamaludin, K. R.
Ramlie, F.
Muhtazaruddin, M. N.
Che Razali, C. M.
W. Muhamad, W. Z. A.
author_sort Harudin, N.
title Binary particle swarm optimization for variables selection optimization Taguchi's T method
title_short Binary particle swarm optimization for variables selection optimization Taguchi's T method
title_full Binary particle swarm optimization for variables selection optimization Taguchi's T method
title_fullStr Binary particle swarm optimization for variables selection optimization Taguchi's T method
title_full_unstemmed Binary particle swarm optimization for variables selection optimization Taguchi's T method
title_sort binary particle swarm optimization for variables selection optimization taguchi's t method
publisher Penerbit UTM Press
publishDate 2020
url http://eprints.utm.my/id/eprint/93320/1/NoliaHarudin2020_BinaryParticleSwarmOptimization.pdf
http://eprints.utm.my/id/eprint/93320/
http://dx.doi.org/10.11113/matematika.v36.n1.1181
_version_ 1717093452437520384
score 13.211869

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