Opposition-Based Learning Binary Bat Algorithm as Feature Selection Approach in Taguchi's T-Method
Prediction modeling has emerged as a powerful tool in various fields, from healthcare to finance, climate science to marketing. One of the prediction modelling techniques available is known as Taguchi's T-method introduced by Dr. Genichi Taguchi. In the T-method prediction model, optimization o...
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Institute of Electrical and Electronics Engineers Inc.
2024
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| _version_ | 1833350940211544064 |
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| author | Marlan Z.M. Jamaludin K.R. Harudin N. |
| author2 | 57223885180 |
| author_facet | 57223885180 Marlan Z.M. Jamaludin K.R. Harudin N. |
| author_sort | Marlan Z.M. |
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| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Prediction modeling has emerged as a powerful tool in various fields, from healthcare to finance, climate science to marketing. One of the prediction modelling techniques available is known as Taguchi's T-method introduced by Dr. Genichi Taguchi. In the T-method prediction model, optimization of the model's accuracy is performed through feature selection process by utilizing an orthogonal array. However, the outcome yielded a sub-optimal result as the orthogonal array has limitation involving a fixed and limited combination used and lack of higher order feature combination in the analysis. Thus, this study proposed an Opposition-based Learning Binary Bat Algorithm as the feature selection technique in the T-method. Based on the experimental results, the proposed feature selection method successfully found a superior combination that yields a better result in terms of the objective function. The proposed method recorded a 77.8% reduction rate of the number of features from 18 to 4. In terms of prediction accuracy, the new T-method prediction model successfully improved 15.9% as compared to the model without feature selection and the T-method with conventional orthogonal array approach. These results suggest that the new T-method prediction model is better in predicting the output even when only 4 features incorporated in the model. � 2023 IEEE. |
| format | Conference Paper |
| id | my.uniten.dspace-34407 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2024 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | my.uniten.dspace-344072024-10-14T11:19:34Z Opposition-Based Learning Binary Bat Algorithm as Feature Selection Approach in Taguchi's T-Method Marlan Z.M. Jamaludin K.R. Harudin N. 57223885180 26434395500 56319654100 Binary Bat Algorithm Feature Selection Opposition-Based Learning Prediction Model Taguchi's T-method Forecasting Learning algorithms Learning systems Bat algorithms Binary bat algorithm Climate science Features selection Model optimization Modelling techniques Opposition-based learning Orthogonal array Prediction modelling Taguchi T-method Feature Selection Prediction modeling has emerged as a powerful tool in various fields, from healthcare to finance, climate science to marketing. One of the prediction modelling techniques available is known as Taguchi's T-method introduced by Dr. Genichi Taguchi. In the T-method prediction model, optimization of the model's accuracy is performed through feature selection process by utilizing an orthogonal array. However, the outcome yielded a sub-optimal result as the orthogonal array has limitation involving a fixed and limited combination used and lack of higher order feature combination in the analysis. Thus, this study proposed an Opposition-based Learning Binary Bat Algorithm as the feature selection technique in the T-method. Based on the experimental results, the proposed feature selection method successfully found a superior combination that yields a better result in terms of the objective function. The proposed method recorded a 77.8% reduction rate of the number of features from 18 to 4. In terms of prediction accuracy, the new T-method prediction model successfully improved 15.9% as compared to the model without feature selection and the T-method with conventional orthogonal array approach. These results suggest that the new T-method prediction model is better in predicting the output even when only 4 features incorporated in the model. � 2023 IEEE. Final 2024-10-14T03:19:34Z 2024-10-14T03:19:34Z 2023 Conference Paper 10.1109/ICSPC59664.2023.10420191 2-s2.0-85186661092 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186661092&doi=10.1109%2fICSPC59664.2023.10420191&partnerID=40&md5=61194d9d651a445d51d63690dcf1d2b3 https://irepository.uniten.edu.my/handle/123456789/34407 107 112 Institute of Electrical and Electronics Engineers Inc. Scopus |
| spellingShingle | Binary Bat Algorithm Feature Selection Opposition-Based Learning Prediction Model Taguchi's T-method Forecasting Learning algorithms Learning systems Bat algorithms Binary bat algorithm Climate science Features selection Model optimization Modelling techniques Opposition-based learning Orthogonal array Prediction modelling Taguchi T-method Feature Selection Marlan Z.M. Jamaludin K.R. Harudin N. Opposition-Based Learning Binary Bat Algorithm as Feature Selection Approach in Taguchi's T-Method |
| title | Opposition-Based Learning Binary Bat Algorithm as Feature Selection Approach in Taguchi's T-Method |
| title_full | Opposition-Based Learning Binary Bat Algorithm as Feature Selection Approach in Taguchi's T-Method |
| title_fullStr | Opposition-Based Learning Binary Bat Algorithm as Feature Selection Approach in Taguchi's T-Method |
| title_full_unstemmed | Opposition-Based Learning Binary Bat Algorithm as Feature Selection Approach in Taguchi's T-Method |
| title_short | Opposition-Based Learning Binary Bat Algorithm as Feature Selection Approach in Taguchi's T-Method |
| title_sort | opposition-based learning binary bat algorithm as feature selection approach in taguchi's t-method |
| topic | Binary Bat Algorithm Feature Selection Opposition-Based Learning Prediction Model Taguchi's T-method Forecasting Learning algorithms Learning systems Bat algorithms Binary bat algorithm Climate science Features selection Model optimization Modelling techniques Opposition-based learning Orthogonal array Prediction modelling Taguchi T-method Feature Selection |
| url_provider | http://dspace.uniten.edu.my/ |