| Summary: | 碩士 === 淡江大學 === 統計學系碩士班 === 101 === The credit card market has been growing rapidly in recent years but the careless authorization of credit cards made the risk of banks increased. Card debt crisis was occured in 2005 and the banks at Taiwan suffered great loss.
Credit card approval relies on past credit performance and applicant''s personal information, but the amount of information is quite large. In this study, we establish prediction models of approval classification by two-stage methods. First, important attributes are selected by F-score and principal component analysis, combined with five different classifiers which are logistic regression, random forest, support vector machines, C4.5 and C5.0, to establish approval models. The average accuracy, sensitivity and specificity of each approach are compared in combination with different classifiers. Our study shows that the two-stage model is better than original classification methods. Reduction of the variables also enhance the computational efficiency.
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