A novel approach for designing adaptive fuzzy classifiers based on the combination of an artificial immune network and a memetic algorithm

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dc.contributor.authorAcılar, Ayşe Merve
dc.contributor.authorArslan, Ahmet
dc.date.accessioned2020-03-26T18:49:13Z
dc.date.available2020-03-26T18:49:13Z
dc.date.issued2014
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn this study, we propose a novel approach for designing fuzzy classifiers. The first part of our approach is a new preprocess algorithm called SPP (silhouette cluster validity index aided pre-process via k-means). The SPP algorithm has been performed on the data set to determine the numbers of the membership functions and their initial boundaries. Then, the Mopt-aiNetLS algorithm (modified version of opt-aiNet combined with local search strategy of memetic algorithm), the second part of the approach; examines search space to find the optimal values of fuzzy rules and membership functions for the system. The Mopt-aiNetLS is the combination of the memetic algorithm and a modified version of the opt-aiNet algorithm, in which some changes were made in the suppression and hypermutation mechanisms of the original opt-aiNet algorithm. These two new mechanisms are called the intelligent suppression mechanism and the adaptive hypermutation operator. Combining the modified version of opt-aiNet with the local search strategy of the memetic algorithm improves the accuracy of the classification rate. An effective search process has been realized using the Mopt-aiNetLS because the global search capability of opt-aiNet is complemented by the local search strategy of the memetic algorithm. To test the performance of this new approach, twenty different well-known classification dataset benchmark problems from the UCI dataset were used. The average 3 x 10 cross-fold validation results obtained from these datasets are presented and compared with the results of certain classification algorithms reported in the literature. The Wilcoxon Signed-Rank Test was also used for statistical comparisons. The obtained results demonstrate the effectiveness of the proposed approach. (C) 2013 Elsevier Inc. All rights reserved.en_US
dc.description.sponsorshipSelcuk University Scientific Research ProjectsSelcuk University; TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)en_US
dc.description.sponsorshipThe authors acknowledge the support of this study provided by Selcuk University Scientific Research Projects. The authors would also like to thank TUBITAK for their support on the study and the anonymous reviewers for their valuable comments and suggestions for improving the article content.en_US
dc.identifier.doi10.1016/j.ins.2013.12.023en_US
dc.identifier.endpage181en_US
dc.identifier.issn0020-0255en_US
dc.identifier.issn1872-6291en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage158en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.ins.2013.12.023
dc.identifier.urihttps://hdl.handle.net/20.500.12395/30559
dc.identifier.volume264en_US
dc.identifier.wosWOS:000333492500012en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIER SCIENCE INCen_US
dc.relation.ispartofINFORMATION SCIENCESen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectFuzzy classifier systemen_US
dc.subjectArtificial immune networken_US
dc.subjectOptimizationen_US
dc.subjectOpt-aiNet algorithmen_US
dc.subjectMemetic algorithmen_US
dc.titleA novel approach for designing adaptive fuzzy classifiers based on the combination of an artificial immune network and a memetic algorithmen_US
dc.typeArticleen_US

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