A Hybrid Bi-level Metaheuristic for Credit Scoring
Küçük Resim Yok
Tarih
2020
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This 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.
Açıklama
Anahtar Kelimeler
Support Vector Machine, Genetic Algorithm, Credit Scoring, Classification, Feature Selection, Feature-Selection, Genetic Algorithm, Financial Ratios, Rough Set, Prediction, Search, Models, Svm, System
Kaynak
Information Systems Frontiers
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
22
Sayı
5
