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Enhanced Taguchi's T-method using angle modulated Bat algorithm for prediction

Marlan, Zulkifli Marlah and Ramlie, Faizir and Jamaludin, Khairur Rijal and Harudin, Nolia (2022) Enhanced Taguchi's T-method using angle modulated Bat algorithm for prediction. Bulletin of Electrical Engineering and Informatics, 11 (5). pp. 2828-2835. ISSN 2089-3191


Official URL: http://dx.doi.org/10.11591/eei.v11i5.4350


Analysis of multivariate historical information in predicting future state or unknown outcomes is the core function of Taguchi’s T-method. Introduced by Dr. Genichi Taguchi under Mahalanobis-Taguchi system, the T-method combines regression principle and robust quality engineering element in formulating a predictive model and employs taguchi’s orthogonal array design in optimizing the model through feature or variable selection process. There is a concern regarding the sub-optimality of the T-method prediction accuracy, particularly when the orthogonal array failed to offer a significant number of combinations in search for an optimal subset of features. This is due to the fixed and limited combination offered for evaluation as well as the lack of higher-order interaction of combination. In response to this issue, this paper proposed an angle modulated Bat algorithm to be integrated with the T-method in optimizing the prediction model. A comparison study was conducted using energy efficiency benchmark datasets with the mean absolute error metric used as the performance measure. The results show that the proposed method improved the prediction accuracy by 10.74%, from 6.05 to 5.4, by integrating only four features over the original eight in the prediction model.

Item Type:Article
Uncontrolled Keywords:angle modulation, binary Bat algorithm, feature selection, Mahalanobis-Taguchi system, Taguchi’s T-method
Subjects:Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Razak School of Engineering and Advanced Technology
ID Code:98611
Deposited By: Yanti Mohd Shah
Deposited On:25 Jan 2023 17:34
Last Modified:25 Jan 2023 17:34

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