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

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cris.virtual.department Universiti Malaysia Perlis
cris.virtual.department Universiti Malaysia Perlis
cris.virtual.department Universiti Malaysia Perlis
cris.virtual.department Universiti Malaysia Perlis
cris.virtualsource.department f706deee-19e1-46a5-a4e8-25c727ea8dbc
cris.virtualsource.department a5cb11b7-6b35-4ac1-a4c6-2313b806448f
cris.virtualsource.department 27a87c47-b118-46f7-be0d-4ade9c595e8c
cris.virtualsource.department ff518d5c-ba64-4fac-a0a1-38ac62497ac6
dc.contributor.author Chow Yong Huan
dc.contributor.author Wan Zuki Azman Wan Muhamad
dc.contributor.author Zainor Ridzuan Yahya
dc.contributor.author Nor Hizamiyani Abdul Azziz
dc.contributor.author Tan Chye Lih
dc.contributor.author Tan Xiao Jian
dc.date.accessioned 2025-08-26T00:37:55Z
dc.date.available 2025-08-26T00:37:55Z
dc.date.issued 2023
dc.description.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.
dc.identifier.doi 10.37934/araset.31.3.93105
dc.identifier.uri https://hdl.handle.net/20.500.14170/14407
dc.language.iso en
dc.publisher Semarak Ilmu Publishing
dc.relation.funding Ministry of Higher Education, Malaysia
dc.relation.grantno #PLACEHOLDER_PARENT_METADATA_VALUE#
dc.relation.ispartof Journal of Advanced Research in Applied Sciences and Engineering Technology
dc.relation.issn 2462-1943
dc.subject Binary Whale Optimisation Algorithm
dc.subject feature selection
dc.subject Mahalanobis Taguchi System
dc.subject Wisconsin Breast Cancer dataset
dc.title Hybrid Mahalanobis Taguchi System with Binary Whale Optimisation Feature Selection for the Wisconsin Breast Cancer Dataset
dc.type journal-article
dspace.entity.type Publication
oaire.citation.endPage 13
oaire.citation.issue 3
oaire.citation.startPage 1
oaire.citation.volume 31
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Tunku Abdul Rahman
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