In this paper, a classification model based on ensemble approach is used for credit scoring of the bank costumers. The proposed method is based on the bagging scheme for the support vector machines classifier. First, the data set is divided into several subsets via the bootstrap method and the support vector machines classifier is implemented on each subsets. Then the final model is made by voting among all of the classifiers. The proposed method has many advantages for implementation, including reduction of computational costs. Two credit data sets are used to show the efficiency and applicability of the present method.
Hoseini,S M , Ebtia,M and Khochiani,R . (2024). An ensemble method based on bagging SVM for credit rating problem. (e114424). Soft Computing Journal, (), e114424 doi: 10.22052/scj.2024.246330.1063
MLA
Hoseini,S M , , Ebtia,M , and Khochiani,R . "An ensemble method based on bagging SVM for credit rating problem" .e114424 , Soft Computing Journal, , , 2024, e114424. doi: 10.22052/scj.2024.246330.1063
HARVARD
Hoseini S M, Ebtia M, Khochiani R. (2024). 'An ensemble method based on bagging SVM for credit rating problem', Soft Computing Journal, (), e114424. doi: 10.22052/scj.2024.246330.1063
CHICAGO
S M Hoseini, M Ebtia and R Khochiani, "An ensemble method based on bagging SVM for credit rating problem," Soft Computing Journal, (2024): e114424, doi: 10.22052/scj.2024.246330.1063
VANCOUVER
Hoseini S M, Ebtia M, Khochiani R. An ensemble method based on bagging SVM for credit rating problem. SCJ. 2024;():e114424 (In Persian). doi: 10.22052/scj.2024.246330.1063
