A fast feature selection approach based on extreme learning machine and coefficient of variation

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dc.authorid0000-0001-7789-6376en_US
dc.authorid0000-0003-0710-0867en_US
dc.contributor.authorErtuğrul, Ömer Faruk
dc.contributor.authorTağluk, Mehmet Emin
dc.date.accessioned2019-05-21T07:58:35Z
dc.date.available2019-05-21T07:58:35Z
dc.date.issued2017-07-30
dc.departmentBatman Üniversitesi Mühendislik - Mimarlık Fakültesi Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractFeature selection is the method of reducing the size of data without degrading their accuracy. In this study, we propose a novel feature selection approach, based on extreme learning machines (ELMs) and the coefficient of variation (CV). In the proposed approach, the most relevant features are identified by ranking each feature with the coefficient obtained through ELM divided by CV. The achieved accuracies and computational costs, obtained with the use of features selected via the proposed approach in 9 classification and 26 regression benchmark data sets, were compared to those obtained with all features, as well as those obtained with the features selected by a wrapper and a filtering method. The achieved accuracy values obtained with the proposed approach were generally higher than when using all features. Furthermore, high feature reduction ratios were obtained with the proposed approach, including the achieved feature reduction ratios in epilepsy, liver, EMG, shuttle, and abalone. Stock data sets were 90.48%, 90%, 70.59%, 66.67%, 75%, and 77.78%, respectively. This approach is an extremely fast process that is independent of the employed machine-learning methods.en_US
dc.identifier.citationErtuğrul, Ö. F., Tağluk, M. E. (2017). A fast feature selection approach based on extreme learning machine and coefficient of variation. Turkish Journal of Electrical Engineering & Computer Sciences, 25 (4), pp. 3409 - 3420. https://doi.org/10.3906/elk-1606-122en_US
dc.identifier.endpage3420en_US
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage3409en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12402/2017
dc.identifier.volume25en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTÜBİTAKen_US
dc.relation.isversionof10.3906/elk-1606-122en_US
dc.relation.journalTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectFeature Selectionen_US
dc.subjectExtreme Learning Machineen_US
dc.subjectCoefficient of Variationen_US
dc.titleA fast feature selection approach based on extreme learning machine and coefficient of variationen_US
dc.typeArticleen_US

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