A new hybrid feature reduction method by using MCMSTClustering algorithm with various feature projection methods: a case study on sleep disorder diagnosis

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dc.authoridSENOL, Ali/0000-0003-0364-2837
dc.authoridTALAN, TARIK/0000-0002-5371-4520
dc.contributor.authorSenol, Ali
dc.contributor.authorTalan, Tarik
dc.contributor.authorAkturk, Cemal
dc.date.accessioned2025-03-17T12:27:31Z
dc.date.available2025-03-17T12:27:31Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.description.abstractIn the machine learning area, having a large number of irrelevant or less relevant features to the result of the dataset can reduce classification success and run-time performance. For this reason, feature selection or reduction methods are widely used. The aim is to eliminate irrelevant features or transform the features into new features that have fewer numbers and are relevant to the results. However, in some cases, feature reduction methods are not sufficient on their own to increase success. In this study, we propose a new hybrid feature projection model to increase the classification performance of classifiers. For this goal, the MCMSTClustering algorithm is used in the data preprocessing stage of classification with various feature projection methods, which are PCA, LDA, SVD, t-SNE, NCA, Isomap, and PR, to increase the classification performance of the sleep disorder diagnosis. To determine the best parameters of the MCMSTClustering algorithm, we used the VIASCKDE Index, Dunn Index, Silhouette Index, Adjusted Rand Index, and Accuracy as cluster quality evaluation methods. To evaluate the performance of the proposed model, we first appended class labels produced by the MCMSTClustering to the dataset as a new feature. We applied selected feature projection methods to decrease the number of features. Then, we performed the kNN algorithm on the dataset. Finally, we compared the obtained results. To reveal the efficiency of the proposed model, we tested it on a sleep disorder diagnosis dataset and compared it with two models that were pure kNN and kNN with the feature projection methods used in the proposed approach. According to the experimental results, the proposed method, in which the feature projection method was Kernel PCA, was the best model with a classification accuracy of 0.9627. In addition, the MCMSTClustering algorithm increases the performance of PCA, Kernel PCA, SVD, t-SNE, and PR. However, the performance of the LDA, NCA, and Isomap remains the same.
dc.identifier.doi10.1007/s11760-024-03097-1
dc.identifier.endpage4603
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85189019414
dc.identifier.scopusqualityQ2
dc.identifier.startpage4589
dc.identifier.urihttps://doi.org/10.1007/s11760-024-03097-1
dc.identifier.urihttps://hdl.handle.net/20.500.13099/2302
dc.identifier.volume18
dc.identifier.wosWOS:001194615800001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofSignal Image and Video Processing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250316
dc.subjectFeature projection
dc.subjectBreast cancer diagnosis
dc.subjectMCMSTClustering
dc.subjectClassification
dc.titleA new hybrid feature reduction method by using MCMSTClustering algorithm with various feature projection methods: a case study on sleep disorder diagnosis
dc.typeArticle

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