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Taguchi?s T-method with Normalization-Based Binary Bat Algorithm

Taguchi?s T-method (T-method) is a predictive modeling technique developed by Dr. Genichi Taguchi under the Mahalanobis-Taguchi system to predict unknown output or future state based on multivariable input variables. Conventionally, Taguchi?s orthogonal array is used as a variable selection approach...

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Main Authors: Marlan Z.M., Jamaludin K.R., Harudin N.
Other Authors: 57223885180
Format: Conference paper
Published: Springer Science and Business Media Deutschland GmbH 2025
Subjects:
Multivariable systems
Bat algorithms
Binary bat algorithm
Discretizations
Input variables
Normalisation
Orthogonal array
Prediction accuracy
Predictive models
Taguchi?s T-method
Variables selections
Forecasting
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spelling my.uniten.dspace-371632025-03-03T15:48:09Z Taguchi?s T-method with Normalization-Based Binary Bat Algorithm Marlan Z.M. Jamaludin K.R. Harudin N. 57223885180 26434395500 56319654100 Multivariable systems Bat algorithms Binary bat algorithm Discretizations Input variables Normalisation Orthogonal array Prediction accuracy Predictive models Taguchi?s T-method Variables selections Forecasting Taguchi?s T-method (T-method) is a predictive modeling technique developed by Dr. Genichi Taguchi under the Mahalanobis-Taguchi system to predict unknown output or future state based on multivariable input variables. Conventionally, Taguchi?s orthogonal array is used as a variable selection approach in optimizing the predictive model. Due to a fixed and restricted predetermined design array, the orthogonal array is unable to give higher-order interactions between variables, resulting in inferior T-method prediction accuracy. Therefore, a variable selection technique using a swarm-based Binary Bat algorithm is proposed. Specifically, a normalization-based Binary Bat algorithm is used, where discretization of continuous solution into binary form is performed using a normalization equation. An experimental study was conducted, and the variable selection process using the normalization-based Binary Bat algorithm found a better combination of input variables which consists of only six out of eight variables. The prediction accuracy was also enhanced by 7.15% when validated using the validation dataset. In conclusion, the proposed method successfully yields better prediction accuracy as compared to conventional approaches. After the variable selection process using the proposed method, the optimal prediction model is now formulated with a lesser variable, making it less complex and computationally fast. ? The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. Final 2025-03-03T07:48:09Z 2025-03-03T07:48:09Z 2024 Conference paper 10.1007/978-3-031-53960-2_27 2-s2.0-85189550364 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189550364&doi=10.1007%2f978-3-031-53960-2_27&partnerID=40&md5=fd4f0f55dd0e9aa4df19952a5d35b860 https://irepository.uniten.edu.my/handle/123456789/37163 919 LNNS 414 428 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Multivariable systems
Bat algorithms
Binary bat algorithm
Discretizations
Input variables
Normalisation
Orthogonal array
Prediction accuracy
Predictive models
Taguchi?s T-method
Variables selections
Forecasting
spellingShingle Multivariable systems
Bat algorithms
Binary bat algorithm
Discretizations
Input variables
Normalisation
Orthogonal array
Prediction accuracy
Predictive models
Taguchi?s T-method
Variables selections
Forecasting
Marlan Z.M.
Jamaludin K.R.
Harudin N.
Taguchi?s T-method with Normalization-Based Binary Bat Algorithm
description Taguchi?s T-method (T-method) is a predictive modeling technique developed by Dr. Genichi Taguchi under the Mahalanobis-Taguchi system to predict unknown output or future state based on multivariable input variables. Conventionally, Taguchi?s orthogonal array is used as a variable selection approach in optimizing the predictive model. Due to a fixed and restricted predetermined design array, the orthogonal array is unable to give higher-order interactions between variables, resulting in inferior T-method prediction accuracy. Therefore, a variable selection technique using a swarm-based Binary Bat algorithm is proposed. Specifically, a normalization-based Binary Bat algorithm is used, where discretization of continuous solution into binary form is performed using a normalization equation. An experimental study was conducted, and the variable selection process using the normalization-based Binary Bat algorithm found a better combination of input variables which consists of only six out of eight variables. The prediction accuracy was also enhanced by 7.15% when validated using the validation dataset. In conclusion, the proposed method successfully yields better prediction accuracy as compared to conventional approaches. After the variable selection process using the proposed method, the optimal prediction model is now formulated with a lesser variable, making it less complex and computationally fast. ? The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
author2 57223885180
author_facet 57223885180
Marlan Z.M.
Jamaludin K.R.
Harudin N.
format Conference paper
author Marlan Z.M.
Jamaludin K.R.
Harudin N.
author_sort Marlan Z.M.
title Taguchi?s T-method with Normalization-Based Binary Bat Algorithm
title_short Taguchi?s T-method with Normalization-Based Binary Bat Algorithm
title_full Taguchi?s T-method with Normalization-Based Binary Bat Algorithm
title_fullStr Taguchi?s T-method with Normalization-Based Binary Bat Algorithm
title_full_unstemmed Taguchi?s T-method with Normalization-Based Binary Bat Algorithm
title_sort taguchi?s t-method with normalization-based binary bat algorithm
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2025
_version_ 1825816206955773952
score 13.244109

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