Economic Growth Prediction Using Optimized Support Vector Machines

dc.contributor.authorEmsia, Elmira
dc.contributor.authorCoskuner, Cagay
dc.date.accessioned2026-02-06T18:34:20Z
dc.date.issued2016
dc.departmentDoğu Akdeniz Üniversitesi
dc.description.abstractThe main objective of this research is to propose a new hybrid model called genetic algorithms-support vector regression (GA-SVR). The proposed model consists of three stages. In the first stage, after lag selection, the most efficient features are selected using stepwise regression algorithm (SRA). Afterward, these variables are used in order to develop proposed model, in which the model uses support vector machines that the parameters of which are tuned by GA. Finally, evaluation of the proposed model is carried out by applying it on the test data set.
dc.identifier.doi10.1007/s10614-015-9528-1
dc.identifier.endpage462
dc.identifier.issn0927-7099
dc.identifier.issn1572-9974
dc.identifier.issue3
dc.identifier.orcid0000-0001-8317-4206
dc.identifier.scopus2-s2.0-84944632668
dc.identifier.scopusqualityQ1
dc.identifier.startpage453
dc.identifier.urihttps://doi.org/10.1007/s10614-015-9528-1
dc.identifier.urihttps://hdl.handle.net/11129/11753
dc.identifier.volume48
dc.identifier.wosWOS:000384209000004
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofComputational Economics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260204
dc.subjectGenetic algorithms
dc.subjectSupport vector regression
dc.subjectStepwise regression algorithm
dc.titleEconomic Growth Prediction Using Optimized Support Vector Machines
dc.typeArticle

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