A Hybrid Bi-level Metaheuristic for Credit Scoring

Basit Öğe Kaydı
dc.authoridSen, Doruk/0000-0003-3353-5952
dc.authorwosidDonmez, Cem Cagri C/E-4859-2019
dc.authorwosidSen, Doruk/D-4547-2016
dc.contributor.authorSen, Doruk
dc.contributor.authorDonmez, Cem Cagri
dc.contributor.authorYildirim, Umman Mahir
dc.date.accessioned2024-07-18T20:40:40Z
dc.date.available2024-07-18T20:40:40Z
dc.date.issued2020
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractThis research aims to propose a framework for evaluating credit applications by assigning a binary score to the applicant. The score is targeted to determine whether the credit application is 'good' or 'bad' in small business purpose loans. Even tiny performance improvements in small businesses may yield a positive impact on the economy as they generate more than 60% of the value. The method presented in this paper hybridizes the Genetic Algorithm (GA) and the Support Vector Machine (SVM) in a bi-level feeding mechanism for increased prediction accuracy. The first level is to determine the parameters of SVM and the second is to find a feature set that increases classification accuracy. To test the proposed approach, we have investigated three different data sets; UCI Australian data set for preliminary works, Lending Club data set for large training and testing, and UCI German and Australian datasets for benchmarking against some other notable methods that use GA. Our computational results show that our proposed method using a feedback mechanism under the hybrid bi-level GA-SVM structure outperforms other classification algorithms in the literature, namely Decision Tree, Random Forests, Logistic Regression, SVM and Artificial Neural Networks, effectively improves the classification accuracy.en_US
dc.identifier.doi10.1007/s10796-020-10037-0
dc.identifier.endpage1019en_US
dc.identifier.issn1387-3326
dc.identifier.issn1572-9419
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85087453388en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1009en_US
dc.identifier.urihttps://doi.org/10.1007/s10796-020-10037-0
dc.identifier.urihttps://hdl.handle.net/11411/7172
dc.identifier.volume22en_US
dc.identifier.wosWOS:000545041500001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofInformation Systems Frontiersen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSupport Vector Machineen_US
dc.subjectGenetic Algorithmen_US
dc.subjectCredit Scoringen_US
dc.subjectClassificationen_US
dc.subjectFeature Selectionen_US
dc.subjectFeature-Selectionen_US
dc.subjectGenetic Algorithmen_US
dc.subjectFinancial Ratiosen_US
dc.subjectRough Seten_US
dc.subjectPredictionen_US
dc.subjectSearchen_US
dc.subjectModelsen_US
dc.subjectSvmen_US
dc.subjectSystemen_US
dc.titleA Hybrid Bi-level Metaheuristic for Credit Scoring
dc.typeArticle

Dosyalar

Koleksiyon

Web of Science Indexed Publications
Scopus Indexed Publications