A survey on improvement of Mahalanobis Taguchi system and its application

Tan, Li Mei; Muhamad, Wan Zuki Azman Wan; Yahya, Zainor Ridzuan; Junoh, Ahmad Kadri; Azziz, Nor Hizamiyani Abdul; Ramlie, Faizir; Harudin, Nolia; Abu, Mohd Yazid; Tan, Xiao Jian

doi:10.1007/s11042-023-15257-5

Cited by 1 publication

(1 citation statement)

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“…Hence, in order to boost performance, Taguchi et al, [10] and Jugulum et al, [19] suggested that the MTS methodology be improved with a better search algorithm. Woodall et al, [11] also reviewed MTS and concluded that another feature selection technique could replace OA [20,21].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Mahalanobis Taguchi System with Binary Whale Optimisation Feature Selection for the Wisconsin Breast Cancer Dataset

Chow Yong Huan,

Wan Zuki Azman Wan Muhamad,

Zainor Ridzuan Yahya

et al. 2023

ARASET

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The Mahalanobis-Taguchi System (MTS) is a statistical approach used in breast cancer research to facilitate early detection and promote efficient treatment. The technique analyses mammogram images for significant features using a multivariate statistical analysis technique. It combines the Mahalanobis distance (MD) and Taguchi's method to determine the differences between benign and malignant samples. While orthogonal array (OA) has been widely used in MTS, it has been criticised for providing suboptimal results due to insufficient coverage of feature combinations during the feature optimisation process. To address this issue, the Binary Whale Optimisation Algorithm (BWOA) is proposed as an improved search algorithm for MTS. This paper aims to develop a novel hybrid method that enhances the efficiency of the Mahalanobis Taguchi System (MTS). The performance of feature selection ability due to different MTS hybrid algorithms were also compared. BWOA simulates the hunting behaviour of humpback whales and works by exploring new regions of the solution space, gradually narrowing the search space, and fine-tuning the solution. MTS-BWOA demonstrated its enhanced capability in feature optimisation compared to traditional MTS methods and has the potential to be applied in other medical imaging domains.

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