A new accurate and efficient approach to extract classification rules [Siniflandirma kurallarinin çikarimi için etkin ve hassas yeni bir yaklaşim]

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dc.contributor.authorKöklü, Murat
dc.contributor.authorKahramanlı, Humar
dc.contributor.authorAllahverdi, Novruz
dc.date.accessioned2020-03-26T18:58:49Z
dc.date.available2020-03-26T18:58:49Z
dc.date.issued2014
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractA new method for extracting rules from multi-class datasets was proposed in this study. The proposed method was applied to 4 different data set. Discrete and real attributes were decoded in different ways. Discrete attributes were coded as binary whereas real attributes were coded by using two real values These values indicate the midpoint and the expansion of intervals of the attributes that form the rules. Classification success was used as fitness function of rule extraction. CLONALG which is Artificial Immune Systems (AIS) algorithm was used to optimize the fitness function. To apply the proposed method Iris, Wine, Glass and Abalone datasets were used. The datasets were obtained from machine learning repository of University of California at Irvine (UCI). The proposed method achieved prediction accuracy ratios of 100%, 99,44%, 77,10%, and 62,59% for Iris, Wine, Glass and Abalone datasets, respectively. When it is compared with the previous studies it has been seen that the proposed method achieved more successful results and has advantage in terms of complexity.en_US
dc.identifier.endpage486en_US
dc.identifier.issn1300-1884en_US
dc.identifier.issue3en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage477en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/31295
dc.identifier.volume29en_US
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherGazi Universitesi Muhendislik-Mimarliken_US
dc.relation.ispartofJournal of the Faculty of Engineering and Architecture of Gazi Universityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectMulti-class problemsen_US
dc.subjectReal value codingen_US
dc.subjectRules extractionen_US
dc.titleA new accurate and efficient approach to extract classification rules [Siniflandirma kurallarinin çikarimi için etkin ve hassas yeni bir yaklaşim]en_US
dc.typeArticleen_US

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