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A survey on improvement of Mahalanobis Taguchi system and its application

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Abstract

Mahalanobis Taguchi System (MTS) is used for pattern recognition and classification, diagnosis, and prediction of a multivariate data set. Mahalanobis Distance (MD), orthogonal array (OA), and signal-to-noise ratio (SNR) are used in traditional MTS in order to identify and optimize the variables. However, the high correlation among variables shows an effect on the inverse of the correlation matrix that uses in the calculation of MD and hence affects the accuracy of the MD. Therefore, Mahalanobis-Taguchi-Gram-Schmidt (MTGS) system is proposed in order to solve the problem of multicollinearity. The value of MD can be calculated by using the Gram-Schmidt Orthogonalization Process (GSOP). Besides, the computational speed and the accuracy in optimization using OA and SNR are other issues that are concerned the authors. Hence, the combination of MTS and other methods such as Binary Particles Swarm Optimization (BPSO) and Binary Ant Colony Optimization (NBACO) is proposed to improve the computational speed and the accuracy in optimization. The purpose of this paper is to review and summarize some works that developed and used the hybrid methodology of MTS as well as its application in several fields. Moreover, a discussion about the future work that can be done related to MTS is carried out.

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Abbreviations

BPSO:

Binary Particle Swarm Optimization

CBPSO:

Chaotic Binary Particle Swarm Optimization

CQPSO:

Chaos Quantum-Behavior Particle Swarm Optimization

DOE:

Design of experiment

GSOP:

Gram-Schmidt Orthogonalization Process

KMD:

Kernel Mahalanonis Distance

MCS:

Mahalanobis Classification System

MD:

Mahalanobis Distance

MMTS:

Multiclass Mahalanobis Taguchi System

MTGS:

Mahalanobis-Taguchi-Gram-Schmidt

MTS:

Mahalanobis Taguchi System

mBA:

Modified-Bee Algorithm

NBACO:

Binary Ant Colony Optimization

OA:

Orthogonal Array

SNR:

Signal-to-Noise Ratio

TVM:

Total Weighted Misclassification

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Acknowledgements

This study was financially supported by the Ministry of Higher Education, Malaysia, under the Fundamental Research Grant Scheme (FRGS/1/2020/STG06/UNIMAP/02/7).

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Authors and Affiliations

  1. Institute of Engineering Mathematics, Universiti Malaysia Perlis, Kampus Pauh Putra, Perlis, 02600, Arau, Malaysia

    Li Mei Tan, Wan Zuki Azman Wan Muhamad, Zainor Ridzuan Yahya, Ahmad Kadri Junoh & Nor Hizamiyani Abdul Azziz

  2. Centre of Excellence for Advanced Computing (ADVCOMP), Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia

    Wan Zuki Azman Wan Muhamad & Zainor Ridzuan Yahya

  3. Razak Faculty of Technology and Informatics, Department of Mechanical Engineering, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia

    Faizir Ramlie

  4. Universiti Tenaga Nasional, 43000, Kajang, Selangor, Malaysia

    Nolia Harudin

  5. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, 26600, Pekan, Malaysia

    Mohd Yazid Abu

  6. Centre for Multimodal Signal Processing, Department of Electrical and Electronic Engineering, Faculty of Engineering and Technology, Tunku Abdul Rahman University of Management and Technology, Jalan Genting Kelang, Setapak, 53300, Kuala Lumpur, Malaysia

    Xiao Jian Tan

  7. Sports Engineering Research Centre (SERC), Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia

    Wan Zuki Azman Wan Muhamad & Xiao Jian Tan

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  1. Li Mei Tan
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Tan, L.M., Wan Muhamad, W.Z.A., Yahya, Z.R. et al. A survey on improvement of Mahalanobis Taguchi system and its application. Multimed Tools Appl 82, 43865–43881 (2023). https://doi.org/10.1007/s11042-023-15257-5

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