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Classification performance of thresholding methods in the Mahalanobis�Taguchi system

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dc.citedby6
dc.contributor.authorRamlie F.en_US
dc.contributor.authorMuhamad W.Z.A.W.en_US
dc.contributor.authorHarudin N.en_US
dc.contributor.authorAbu M.Y.en_US
dc.contributor.authorYahaya H.en_US
dc.contributor.authorJamaludin K.R.en_US
dc.contributor.authorTalib H.H.A.en_US
dc.contributor.authorid55982859700en_US
dc.contributor.authorid55860800560en_US
dc.contributor.authorid56319654100en_US
dc.contributor.authorid55983627200en_US
dc.contributor.authorid57200983401en_US
dc.contributor.authorid26434395500en_US
dc.contributor.authorid35119607000en_US
dc.date.accessioned2023-05-29T09:07:58Z
dc.date.available2023-05-29T09:07:58Z
dc.date.issued2021
dc.description.abstractThe Mahalanobis�Taguchi System (MTS) is a pattern recognition tool employing Maha-lanobis Distance (MD) and Taguchi Robust Engineering philosophy to explore and exploit data in multidimensional systems. The MD metric provides a measurement scale to classify classes of samples (Abnormal vs. Normal) and gives an approach to measuring the level of severity between classes. An accurate classification result depends on a threshold value or a cut-off MD value that can effectively separate the two classes. Obtaining a reliable threshold value is very crucial. An inaccurate threshold value could lead to misclassification and eventually resulting in a misjudgment decision which in some cases caused fatal consequences. Thus, this paper compares the performance of the four most common thresholding methods reported in the literature in minimizing the misclas-sification problem of the MTS namely the Type I�Type II error method, the Probabilistic thresholding method, Receiver Operating Characteristics (ROC) curve method and the Box�Cox transformation method. The motivation of this work is to find the most appropriate thresholding method to be utilized in MTS methodology among the four common methods. The traditional way to obtain a threshold value in MTS is using Taguchi�s Quadratic Loss Function in which the threshold is obtained by minimizing the costs associated with misclassification decision. However, obtaining cost-related data is not easy since monetary related information is considered confidential in many cases. In this study, a total of 20 different datasets were used to evaluate the classification performances of the four different thresholding methods based on classification accuracy. The result indicates that none of the four thresholding methods outperformed one over the others in (if it is not for all) most of the datasets. Nevertheless, the study recommends the use of the Type I�Type II error method due to its less computational complexity as compared to the other three thresholding methods. � 2021 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo3906
dc.identifier.doi10.3390/app11093906
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85105522994
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85105522994&doi=10.3390%2fapp11093906&partnerID=40&md5=ba795f21f72ef8c0f3fd0ddaff978321
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26223
dc.identifier.volume11
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleApplied Sciences (Switzerland)
dc.titleClassification performance of thresholding methods in the Mahalanobis�Taguchi systemen_US
dc.typeArticleen_US
dspace.entity.typePublication
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