Publication: Opposition-Based Learning Binary Bat Algorithm as Feature Selection Approach in Taguchi's T-Method
Date
2023
Authors
Marlan Z.M.
Jamaludin K.R.
Harudin N.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
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.
Description
Keywords
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