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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/94210

    Title: 以兩階段分類法建構信用卡授信決策模型的實務評估
    Other Titles: Empirical evaluation of two-stage classification methods on credit card approval system
    Authors: 巫天虹;Wu, Tien-Hung
    Contributors: 淡江大學統計學系碩士班
    陳景祥;Chen, Ching-Hsiang
    Keywords: 變數選取;信用卡;邏輯斯迴歸;隨機森林;支援向量機;C4.5;C5.0;Feature Selection;Credit card;Logistic Regression;random forest;Support Vector Machine
    Date: 2013
    Issue Date: 2014-01-23 14:09:27 (UTC+8)
    Abstract: 近年來,信用卡業務的成長相當的快速,但對於信用卡審核並不嚴謹,使得風險提高,導致2005年底爆發了卡債風暴,使得金融機構承受莫大的損失。
    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.
    Appears in Collections:[統計學系暨研究所] 學位論文

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