A Hybrid Credit Scoring Model Based on Genetic Programming and Support Vector Machines

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@InProceedings{Zhang:2008:ICNC,

• author = "Defu Zhang and Mhand Hifi and Qingshan Chen and Weiguo Ye",
• title = "A Hybrid Credit Scoring Model Based on Genetic Programming and Support Vector Machines",
• booktitle = "Fourth International Conference on Natural Computation, ICNC '08",
• year = "2008",
• month = oct,
• volume = "7",
• pages = "8--12",
• keywords = "genetic algorithms, genetic programming, UCI database, back-propagation neural network, credit industry, decision tree classifiers, hybrid credit scoring model, logistic regression, support vector machines, financial data processing, support vector machines",
• DOI = "doi:10.1109/ICNC.2008.205",
• 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.",
• notes = "Also known as \cite{4667935}",

}

Genetic Programming entries for De-Fu Zhang Mhand Hifi Qing-Shan Chen Weiguo Ye

Citations