A Two-Phase Model Based on SVM and Conjoint Analysis for Credit Scoring
暂无分享,去创建一个
In this study, we use least square support vector machines (LSSVM) to construct a credit scoring model and introduce conjoint analysis technique to analyze the relative importance of each input feature for making the decision in the model. A test based on a real-world credit dataset shows that the proposed model has good classification accuracy and can help explain the decision. Hence, it is an alternative model for credit scoring tasks.
[1] L. Thomas. A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers , 2000 .
[2] Mu-Chen Chen,et al. Credit scoring with a data mining approach based on support vector machines , 2007, Expert Syst. Appl..
[3] R. Shah,et al. Least Squares Support Vector Machines , 2022 .
[4] Charles P. Staelin. Parameter selection for support vector machines , 2002 .
[5] David West,et al. Neural network credit scoring models , 2000, Comput. Oper. Res..