A grasshopper optimizer approach for feature selection and optimizing SVM parameters utilizing real biomedical data sets

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dc.contributor.authorIbrahim, Hadeel Tariq
dc.contributor.authorMazher, Wamidh Jalil
dc.contributor.authorUçan, Osman Nuri
dc.contributor.authorBayat, Oğuz
dc.date.accessioned2021-05-15T11:34:19Z
dc.date.available2021-05-15T11:34:19Z
dc.date.issued2019
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.descriptionAl-Rayes, Hadeel/0000-0001-9749-4024; mazher, wamidh jalil/0000-0003-2092-3745
dc.description.abstractSupport vector machines (SVM) are one of the important techniques used to solve classifications problems efficiently. Setting support vector machine kernel factors affects the classification performance. Feature selection is a powerful technique to solve dimensionality problems. In this paper, we optimized SVM factors and chose features using a Grasshopper Optimization Algorithm (GOA). GOA is a new heuristic optimization algorithm inspired by grasshoppers searching for food. It approved its ability to solve real-world problems with anonymous search space. We applied the proposed GOA + SVM approach on biomedical data sets for Iraqi cancer patients in 2010-2012 and for University of California Irvine data sets.en_US
dc.identifier.doi10.1007/s00521-018-3414-4
dc.identifier.endpage5974en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-85043386208
dc.identifier.scopusqualityQ1
dc.identifier.startpage5965en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-018-3414-4
dc.identifier.urihttps://hdl.handle.net/20.500.12939/308
dc.identifier.volume31en_US
dc.identifier.wosWOS:000491131700019
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorUçan, Osman Nuri
dc.institutionauthorBayat, Oğuz
dc.institutionauthorIbrahim, Hadeel Tariq
dc.language.isoen
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSupport Vector Machinesen_US
dc.subjectFeature Selectionen_US
dc.subjectGrasshopper Optimizeren_US
dc.titleA grasshopper optimizer approach for feature selection and optimizing SVM parameters utilizing real biomedical data sets
dc.typeArticle

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Bilgisayar Mühendisliği Bölümü Koleksiyonu
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