Opposition-Based Learning Binary Bat Algorithm as Feature Selection Approach in Taguchi's T-Method - Marlan Z.M.; Jamaludin K.R.; Harudin N. (Institute of Electrical and Electronics Engineers Inc., 2024)
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.