Breast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanism

Basit Öğe Kaydı
dc.contributor.authorPolat, Kemal
dc.contributor.authorSahan, Seral
dc.contributor.authorKodaz, Halife
dc.contributor.authorGuenes, Salih
dc.date.accessioned2020-03-26T17:17:04Z
dc.date.available2020-03-26T17:17:04Z
dc.date.issued2007
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractArtificial Immune Recognition System (AIRS) classification algorithm, which has an important place among classification algorithms in the field of Artificial Immune Systems, has showed an effective and intriguing performance on the problems it was applied. AIRS was previously applied to some medical classification problems including Breast Cancer, Cleveland Heart Disease, Diabetes and it obtained very satisfactory results. So, AIRS proved to be an efficient artificial intelligence technique in medical field. In this study, the resource allocation mechanism of AIRS was changed with a new one determined by Fuzzy-Logic. This system, named as Fuzzy-AIRS was used as a classifier in the diagnosis of Breast Cancer and Liver Disorders, which are of great importance in medicine. The classifications of Breast Cancer and BUPA Liver Disorders datasets taken from University of California at Irvine (UCI) Machine Learning Repository were done using 10-fold cross-validation method. Reached classification accuracies were evaluated by comparing them with reported classifiers in UCI web site in addition to other systems that are applied to the related problems. Also, the obtained classification performances were compared with AIRS with regard to the classification accuracy, number of resources and classification time. Fuzzy-AIRS, which reached to classification accuracy of 98.51% for breast cancer, classified the Liver Disorders dataset with 83.36% accuracy. For both datasets, Fuzzy-AIRS obtained the highest classification accuracy according to the UCI web site. Beside of this success, Fuzzy-AIRS gained an important advantage over the AIRS by means of classification time. In the experiments, it was seen that the classification time in Fuzzy-AIRS was reduced about 70% of AIRS for both datasets. By reducing classification time as well as obtaining high classification accuracies in the applied datasets, Fuzzy-AIRS classifier proved that it could be used as an effective classifier for medical problems. (C) 2005 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2005.11.024en_US
dc.identifier.endpage183en_US
dc.identifier.issn0957-4174en_US
dc.identifier.issn1873-6793en_US
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage172en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2005.11.024
dc.identifier.urihttps://hdl.handle.net/20.500.12395/21249
dc.identifier.volume32en_US
dc.identifier.wosWOS:000242836900018en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectfuzzy resource allocationen_US
dc.subjectAIRSen_US
dc.subjectBreast Cancer dataseten_US
dc.subjectLiver Disorders dataseten_US
dc.subjectk-Fold cross-validationen_US
dc.titleBreast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanismen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu