A new classification method using soft decision-making based on an aggregation operator of fuzzy parameterized fuzzy soft matrices

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dc.authoridEnginoglu, Serdar/0000-0002-7188-9893
dc.authoridErkan, Ugur/0000-0002-2481-0230
dc.contributor.authorMemis, Samet
dc.contributor.authorEnginoglu, Serdar
dc.contributor.authorErkan, Ugur
dc.date.accessioned2025-01-27T20:57:53Z
dc.date.available2025-01-27T20:57:53Z
dc.date.issued2022
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractRecently, a precise and stable machine learning algorithm, i.e. eigenvalue classification method (EigenClass), has been developed by using the concept of generalised eigenvalues in contrast to common approaches, such as k-nearest neighbours, support vector machines, and decision trees. In this paper, we offer a new classification algorithm called fuzzy parameterized fuzzy soft aggregation classifier (FPFS-AC) to combine the modelling ability of soft decision-making (SDM) and classification success of generalised eigenvalues. FPFS-AC constructs a decision matrix by employing the similarity measures of fuzzy parameterized fuzzy soft matrices (fpfs-matrices) and a generalised eigenvalue-based similarity measure. Then, it applies an SDM method based on the aggregation operator of fpfs-matrices to a decision matrix and classifies the given test sample. Afterwards, we perform an experimental study using 15 UCI datasets to manifest the success of our approach and compare FPFS-AC with the well-known and state-of-the-art classifiers (kNN, SVM, fuzzy kNN, EigenClass, and BM-fuzzy kNN) in terms of accuracy, precision, recall, macro F-score, micro F-score, and running time. Moreover, we statistically analyse the experimentally obtained data. Experimental and statistical results show that FPFS-AC outperforms the state-of-the-art classifiers in all the datasets concerning the five performance metrics.
dc.description.sponsorship2211-C Domestic Doctoral Fellowship for Priority Areas by the Scientific and Technological Research Council of Turkey (TuBTAK) [1649B031905299]
dc.description.sponsorshipThis research study was granted 2211-C Domestic Doctoral Fellowship for Priority Areas by the Scientific and Technological Research Council of Turkey (TuBTAK) under Grant 1649B031905299.
dc.identifier.doi10.55730/1300-0632.3816
dc.identifier.endpage890
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85128321544
dc.identifier.scopusqualityQ2
dc.identifier.startpage871
dc.identifier.urihttps://doi.org/10.55730/1300-0632.3816
dc.identifier.urihttps://hdl.handle.net/20.500.12428/26538
dc.identifier.volume30
dc.identifier.wosWOS:000774599800024
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTubitak Scientific & Technological Research Council Turkey
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciences
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20250125
dc.subjectFuzzy sets
dc.subjectsoft sets
dc.subjectsoft decision-making (SDM)
dc.subjectfpfs -matrices
dc.subjectsupervised learning
dc.subjectdata classification
dc.titleA new classification method using soft decision-making based on an aggregation operator of fuzzy parameterized fuzzy soft matrices
dc.typeArticle

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