Tolerance rough set firefly-based quick reduct

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dc.authorid0000-0002-2956-3507en_US
dc.authorid0000-0002-7869-6373en_US
dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0002-0527-4858en_US
dc.contributor.authorGanesan, Jothi
dc.contributor.authorInbarani, Hannah H.
dc.contributor.authorAzar, Ahmad Taher
dc.contributor.authorPolat, Kemal
dc.date.accessioned2021-06-23T19:45:16Z
dc.date.available2021-06-23T19:45:16Z
dc.date.issued2017
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractIn medical information system, there are a lot of features and the relationship among elements is solid. In this way, feature selection of medical datasets gets awesome worry as of late. In this article, tolerance rough set firefly-based quick reduct, is developed and connected to issue of differential finding of diseases. The hybrid intelligent framework intends to exploit the advantages of the fundamental models and, in the meantime, direct their restrictions. Feature selection is procedure for distinguishing ideal feature subset of the original features. A definitive point of feature selection is to build the precision, computational proficiency and adaptability of expectation strategy in machine learning, design acknowledgment and information mining applications. Along these lines, the learning framework gets a brief structure without lessening the prescient precision by utilizing just the chose remarkable features. In this research, a hybridization of two procedures, tolerance rough set and as of late created meta-heuristic enhancement calculation, the firefly algorithm is utilized to choose the conspicuous features of medicinal information to have the capacity to characterize and analyze real sicknesses. The exploratory results exhibited that the proficiency of the proposed system outflanks the current supervised feature selection techniques.en_US
dc.identifier.doi10.1007/s00521-016-2514-2
dc.identifier.endpage3008en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-84982883828en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2995en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-016-2514-2
dc.identifier.urihttps://hdl.handle.net/20.500.12491/9125
dc.identifier.volume28en_US
dc.identifier.wosWOS:000426865100013en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectRough Set Theoryen_US
dc.subjectTolerance Rough Seten_US
dc.subjectFirefly Algorithmen_US
dc.subjectSoft Computing Techniquesen_US
dc.subjectSwarm Intelligenten_US
dc.subjectSupervised Feature Selectionen_US
dc.titleTolerance rough set firefly-based quick reducten_US
dc.typeArticleen_US

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