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ESWA
2007

Credit scoring with a data mining approach based on support vector machines

11 years 1 months ago
Credit scoring with a data mining approach based on support vector machines
The credit card industry has been growing rapidly recently, and thus huge numbers of consumers’ credit data are collected by the credit department of the bank. The credit scoring manager often evaluates the consumer’s credit with intuitive experience. However, with the support of the credit classification model, the manager can accurately evaluate the applicant’s credit score. Support Vector Machine (SVM) classification is currently an active research area and successfully solves classification problems in many domains. This study used three strategies to construct the hybrid SVM-based credit scoring models to evaluate the applicant’s credit score from the applicant’s input features. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the SVM classifier. Compared with neural networks, genetic programming, and decision tree classifiers, the SVM classifier achieved an identical classificatory accuracy with relatively ...
Cheng-Lung Huang, Mu-Chen Chen, Chieh-Jen Wang
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2007
Where ESWA
Authors Cheng-Lung Huang, Mu-Chen Chen, Chieh-Jen Wang
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