Rule extraction from trained adaptive neural networks using artificial immune systems

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dc.contributor.authorKahramanli, Humar
dc.contributor.authorAllahverdi, Novruz
dc.date.accessioned2020-03-26T17:40:03Z
dc.date.available2020-03-26T17:40:03Z
dc.date.issued2009
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractAlthough artificial neural network (ANN) usually reaches high classification accuracy, the obtained results sometimes may be incomprehensible. This fact is causing it serious problem in data mining applications. The rules that are derived from ANN are needed to be formed to solve this problem and various methods have been improved to extract these rules. Activation function is critical as the behavior and performance of an ANN model largely depends oil it. So far there have been limited studies with emphasis oil setting a few free parameters in the neuron activation function. ANN's with such activation function Seem to provide better fitting properties than classical architectures with fixed activation function neurons [Xu, S., & Zhang, M. (2005). Data mining - An adaptive neural network model for financial analysis. In Proceedings of the third international conference on information technology and applications]. In this study a new method that uses artificial immune systems (AIS) algorithm has been presented to extract rules from trained adaptive neural network. Two real time problems data were investigated for determining applicability of the proposed method. The data were obtained from University of California at Irvine (UCI) machine learning repository. The datasets were obtained from Breast Cancer disease and ECG data. The proposed method achieved accuracy values 94.59% and 92.3% for ECG and Breast Cancer dataset, respectively. It has been observed that these results arc one of the best results comparing with results obtained from related previous studies and reported in UCI web sites. (c) 2007 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipSelcuk UniversitySelcuk Universityen_US
dc.description.sponsorshipThis study is supported by the Scientific Research Projects Unit of Selcuk University.en_US
dc.identifier.doi10.1016/j.eswa.2007.11.024en_US
dc.identifier.endpage1522en_US
dc.identifier.issn0957-4174en_US
dc.identifier.issn1873-6793en_US
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1513en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2007.11.024
dc.identifier.urihttps://hdl.handle.net/20.500.12395/23838
dc.identifier.volume36en_US
dc.identifier.wosWOS:000262178000056en_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.subjectAdaptive neural networksen_US
dc.subjectArtificial immune systemsen_US
dc.subjectOptimizationen_US
dc.subjectRule extractionen_US
dc.subjectBackpropagationen_US
dc.subjectOpt-aiNETen_US
dc.titleRule extraction from trained adaptive neural networks using artificial immune systemsen_US
dc.typeArticleen_US

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