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頁籤選單縮合
題名 | A Comparitive Study of Support Vector Machine and Logistic Regression in Credit Scorecard Model=支持向量機和邏輯斯回歸信用記分卡模型的比較研究 |
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作者 | Kiruthika; Dilsha M; |
期刊 | International Journal of Information and Management Sciences |
出版日期 | 20151200 |
卷期 | 26:4 2015.12[民104.12] |
頁次 | 頁a9+411-422 |
分類號 | 563.1 |
語文 | eng |
關鍵詞 | 信用記分卡; 邏輯斯回歸; 支援向量機; 輻射基底函數; Credit scorecard; Logistic regression; Support vector machine; Radial basis function; |
英文摘要 | Credit analysts generally assess the risk of credit applications based on their previous experience. They frequently employ quantitative methods to this end. Most of the financial and banking institutions are using logistic regression to build a credit scorecard. Among the new method, Support Vector Machines (SVM) has been applied in various studies of scorecard modelling. SVM classification is currently an active research area and successfully solves classification problems in many domains. This paper uses standard logistic regression models and compares them with the more advanced least squares support vector machine models with linear and radial basis function kernels. A microfinance data set is used to test the model performance. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。