Extracting rules for classification problems: AIS based approach
dc.contributor.author | Kahramanli, Humar | |
dc.contributor.author | Allahverdi, Novruz | |
dc.date.accessioned | 2020-03-26T17:38:40Z | |
dc.date.available | 2020-03-26T17:38:40Z | |
dc.date.issued | 2009 | |
dc.department | Selçuk Üniversitesi | en_US |
dc.description.abstract | Although Artificial Neural Network (ANN) usually reaches high classification accuracy, the obtained results in most cases may be incomprehensible. This fact is causing a 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. In our previous work, a hybrid neural network was presented for classification (Kahramanli & Allahverdi, 2008). In this study a method that uses Artificial Immune Systems (AIS) algorithm has been presented to extract rules from trained hybrid neural network. The data were obtained from the University of California at Irvine (UCI) machine learning repository. The datasets are Cleveland heart disease and Hepatitis data. The proposed method achieved accuracy values 96.4% and 96.8% for Cleveland heart disease dataset and Hepatitis dataset respectively. It is been observed that these results are one of the best results comparing with results obtained from related previous studies and reported in UCI web sites. (C) 2009 Published by Elsevier Ltd. | en_US |
dc.description.sponsorship | Selcuk UniversitySelcuk University | en_US |
dc.description.sponsorship | This study is supported by the Scientific Research Projects Unit of Selcuk University. | en_US |
dc.identifier.doi | 10.1016/j.eswa.2009.01.029 | en_US |
dc.identifier.endpage | 10502 | en_US |
dc.identifier.issn | 0957-4174 | en_US |
dc.identifier.issn | 1873-6793 | en_US |
dc.identifier.issue | 7 | en_US |
dc.identifier.scopusquality | Q1 | en_US |
dc.identifier.startpage | 10494 | en_US |
dc.identifier.uri | https://dx.doi.org/10.1016/j.eswa.2009.01.029 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12395/23543 | |
dc.identifier.volume | 36 | en_US |
dc.identifier.wos | WOS:000266851000034 | en_US |
dc.identifier.wosquality | Q1 | en_US |
dc.indekslendigikaynak | Web of Science | en_US |
dc.indekslendigikaynak | Scopus | en_US |
dc.language.iso | en | en_US |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | en_US |
dc.relation.ispartof | EXPERT SYSTEMS WITH APPLICATIONS | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.selcuk | 20240510_oaig | en_US |
dc.subject | Hybrid neural networks | en_US |
dc.subject | Artificial Immune Systems | en_US |
dc.subject | Optimization | en_US |
dc.subject | Rule extraction | en_US |
dc.subject | Opt-aiNET | en_US |
dc.title | Extracting rules for classification problems: AIS based approach | en_US |
dc.type | Article | en_US |