Abstract
Credit scoring has obtained more and more attention as the credit industry can benefit from reducing potential risks. Hence, many different useful techniques, known as the credit scoring models, have been developed by the banks and researchers in order to solve the problems involved during the evaluation process. In this paper, a hybrid credit scoring model (HCSM) is developed to deal with the credit scoring problem by incorporating the advantages of genetic programming and support vector machines. Two credit data sets in UCI database are selected as the experimental data to demonstrate the classification accuracy of the HCSM. Compared with support vector machines, genetic programming, decision tree classifiers, logistic regression, and back-propagation neural network, HCSM can obtain better classification accuracy.
Published Version
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