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dc.contributor.authorMemiş, Samet
dc.contributor.authorArslan, Burak
dc.contributor.authorAydın, Tuğce
dc.contributor.authorEnginoğlu, Serdar
dc.contributor.authorCamcı, Çetin
dc.date.accessioned2024-01-24T06:57:29Z
dc.date.available2024-01-24T06:57:29Z
dc.date.issued2023en_US
dc.identifier.citationMemiş, S., Arslan, B., Aydın, T., Enginoğlu, S., & Camcı, Ç. (2023). Distance and Similarity Measures of Intuitionistic Fuzzy Parameterized Intuitionistic Fuzzy Soft Matrices and Their Applications to Data Classification in Supervised Learning. Axioms, 12(5). https://doi.org/10.3390/axioms12050463en_US
dc.identifier.issn2075-1680
dc.identifier.urihttps://doi.org/10.3390/axioms12050463
dc.identifier.urihttps://hdl.handle.net/20.500.12428/5366
dc.description.abstractIntuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices (ifpifs-matrices), proposed by Enginoğlu and Arslan in 2020, are worth utilizing in data classification in supervised learning due to coming into prominence with their ability to model decision-making problems. This study aims to define the concepts metrics, quasi-, semi-, and pseudo-metrics and similarities, quasi-, semi-, and pseudo-similarities over ifpifs-matrices; develop a new classifier by using them; and apply it to data classification. To this end, it develops a new classifier, i.e., Intuitionistic Fuzzy Parameterized Intuitionistic Fuzzy Soft Classifier (IFPIFSC), based on six pseudo-similarities proposed herein. Moreover, this study performs IFPIFSC’s simulations using 20 datasets provided in the UCI Machine Learning Repository and obtains its performance results via five performance metrics, accuracy (Acc), precision (Pre), recall (Rec), macro F-score (MacF), and micro F-score (MicF). It also compares the aforementioned results with those of 10 well-known fuzzy-based classifiers and 5 non-fuzzy-based classifiers. As a result, the mean Acc, Pre, Rec, MacF, and MicF results of IFPIFSC, in comparison with fuzzy-based classifiers, are 94.45%, 88.21%, 86.11%, 87.98%, and 89.62%, the best scores, respectively, and with non-fuzzy-based classifiers, are 94.34%, 88.02%, 85.86%, 87.65%, and 89.44%, the best scores, respectively. Later, this study conducts the statistical evaluations of the performance results using a non-parametric test (Friedman) and a post hoc test (Nemenyi). The critical diagrams of the Nemenyi test manifest the performance differences between the average rankings of IFPIFSC and 10 of the 15 are greater than the critical distance (4.0798). Consequently, IFPIFSC is a convenient method for data classification. Finally, to present opportunities for further research, this study discusses the applications of ifpifs-matrices for machine learning and how to improve IFPIFSC.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subjectDistance measuresen_US
dc.subjectIfpifs-matricesen_US
dc.subjectIntuitionistic fuzzy setsen_US
dc.subjectMachine learningen_US
dc.subjectSimilarity measuresen_US
dc.subjectSoft setsen_US
dc.titleDistance and Similarity Measures of Intuitionistic Fuzzy Parameterized Intuitionistic Fuzzy Soft Matrices and Their Applications to Data Classification in Supervised Learningen_US
dc.typearticleen_US
dc.authorid-en_US
dc.authorid0000-0002-8134-1004en_US
dc.authorid0000-0002-7188-9893en_US
dc.authorid0000-0002-0122-559Xen_US
dc.relation.ispartofAxiomsen_US
dc.departmentFakülteler, Fen Fakültesi, Matematik Bölümüen_US
dc.identifier.volume12en_US
dc.identifier.issue5en_US
dc.institutionauthorArslan, Burak
dc.institutionauthorAydın, Tuğce
dc.institutionauthorEnginoğlu, Serdar
dc.institutionauthorCamcı, Çetin
dc.identifier.doi10.3390/axioms12050463en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.authorwosid-en_US
dc.authorwosidAAT-9038-2020en_US
dc.authorwosidK-1181-2012en_US
dc.authorwosid-en_US
dc.authorscopusid57219948784en_US
dc.authorscopusid57217678344en_US
dc.authorscopusid35772373300en_US
dc.authorscopusid7005108545en_US
dc.identifier.wosqualityQ2en_US
dc.identifier.wosWOS:001011244900001en_US
dc.identifier.scopus2-s2.0-85160242194en_US


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