EVOLVING RULES FROM NEURAL NETWORKS TRAINED ON BINARY AND CONTINUOUS DATA

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dc.contributor.authorKahramanli, Humar
dc.contributor.authorAllahverdi, Novruz
dc.date.accessioned2020-03-26T17:48:37Z
dc.date.available2020-03-26T17:48:37Z
dc.date.issued2010
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractAlthough an Artificial Neural Network (ANN) usually reaches high classification accuracy, the obtained results sometimes may be incomprehensible. This fact is causing a serious problem in data mining applications. The rules that are derived from an ANN need to be formed to solve this problem and various methods have been improved to extract these rules. In this study, a new method that uses an Artificial Immune Systems (AIS) algorithm has been presented to extract rules from a trained ANN. The suggested algorithm does not depend on the ANN training algorithms; also, it does not modify the training results. This algorithm takes all input attributes into consideration and extracts rules from a trained neural network efficiently. This study demonstrates the use of AIS algorithms for extracting rules from trained neural networks. The approach consists of three phases: 1. data coding 2. classification of the coded data 3. rule extraction Continuous and noncontinuous values are used together in medical data. Regarding this, two methods are used for data coding and two methods (binary optimisation and real optimisation) are implemented for rule extraction. First, all data are coded binary and the optimal vectors are decoded and used to obtain rules. Then nominal data are coded binary and real data are normalized. After optimization, various intervals for continuous data are obtained and classification accuracy is increased.en_US
dc.identifier.endpage231en_US
dc.identifier.isbn978-1-60456-646-8
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage211en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/24914
dc.identifier.wosWOS:000278665800007en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherNOVA SCIENCE PUBLISHERS, INCen_US
dc.relation.ispartofMACHINE LEARNING RESEARCH PROGRESSen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectartificial neural networksen_US
dc.subjectartificial immune systemsen_US
dc.subjectoptimizationen_US
dc.subjectrule extractionen_US
dc.subjectbackpropagationen_US
dc.subjectOpt-aiNETen_US
dc.titleEVOLVING RULES FROM NEURAL NETWORKS TRAINED ON BINARY AND CONTINUOUS DATAen_US
dc.typeBook Chapteren_US

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