Publication:
Hybrid Mahalanobis Taguchi System with Binary Whale Optimisation Feature Selection for the Wisconsin Breast Cancer Dataset
Hybrid Mahalanobis Taguchi System with Binary Whale Optimisation Feature Selection for the Wisconsin Breast Cancer Dataset
No Thumbnail Available
Date
2023
Authors
Chow Yong Huan
Wan Zuki Azman Wan Muhamad
Zainor Ridzuan Yahya
Nor Hizamiyani Abdul Azziz
Tan Chye Lih
Tan Xiao Jian
Journal Title
Journal ISSN
Volume Title
Publisher
Semarak Ilmu Publishing
Research Projects
Organizational Units
Journal Issue
Abstract
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.
Description
Keywords
Binary Whale Optimisation Algorithm,
feature selection,
Mahalanobis Taguchi System,
Wisconsin Breast Cancer dataset