A new classification method for breast cancer diagnosis: Feature Selection Artificial Immune Recognition System (FS-AIRS)

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dc.contributor.authorPolat, K
dc.contributor.authorSahan, S
dc.contributor.authorKodaz, H
dc.contributor.authorGunes, S
dc.date.accessioned2020-03-26T16:56:26Z
dc.date.available2020-03-26T16:56:26Z
dc.date.issued2005
dc.departmentSelçuk Üniversitesien_US
dc.description1st International Conference on Natural Computation (ICNC 2005) -- AUG 27-29, 2005 -- Changsha, PEOPLES R CHINAen_US
dc.description.abstractIn this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with a new approach, FS-AIRS (Feature Selection Artificial Immune Recognition System) algorithm that has an important place in classification systems and was developed depending on the Artificial Immune Systems. With this purpose, 683 data in the Wisconsin breast cancer dataset (WBCD) was used. In this study, differently from the studies in the literature related to this concept, firstly, the feature number of each data was reduced to 6 from 9 in the feature selection sub-program by means of forming rules related to the breast cancer data with the C4.5 decision tree algorithm. After separating the 683 data set with reduced feature number into training and test sets by 10 fold cross validation method in the second stage, the data set was classified in the third stage with AIRS and a quite satisfying result was obtained with respect to the classification accuracy compared to the other methods used for this classification problem.en_US
dc.description.sponsorshipXiangtang Univ, IEEE Circuits & Syst Soc, IEEE Computat Intelligence Soc, IEEE Control Syst Soc, Int Neural Network Soc, European Neural Network Soc, Chinese Assoc Artificial Intelligence, Japanese Neural Network Soc, Int Fuzzy Syst Assoc, Asia Pacific Neural Network Assembly, Fuzzy Math & Syst Assoc China, Hunan Comp Federaten_US
dc.identifier.endpage838en_US
dc.identifier.isbn3-540-28325-0
dc.identifier.issn0302-9743en_US
dc.identifier.issn1611-3349en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage830en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/19565
dc.identifier.volume3611en_US
dc.identifier.wosWOS:000232222500117en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGER-VERLAG BERLINen_US
dc.relation.ispartofADVANCES IN NATURAL COMPUTATION, PT 2, PROCEEDINGSen_US
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.titleA new classification method for breast cancer diagnosis: Feature Selection Artificial Immune Recognition System (FS-AIRS)en_US
dc.typeConference Objecten_US

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