Credit scoring by feature-weighted support vector machines  

作　　者：Jian SHI Shu-you ZHANG Le-miao QIU 

机构地区：[1]The State Key Laboratory of Fluid Power and Mechatronic Systems,Zhejiang University [2]School of Electrical and Automatic Engineering,Changshu Institute of Technology

出　　处：《Journal of Zhejiang University-Science C(Computers and Electronics)》2013年第3期197-204,共8页浙江大学学报C辑（计算机与电子（英文版）

基　　金：Project supported by the National Basic Research Program (973) of China (No. 2011CB706506);the National Natural Science Foundation of China (No. 50905159);the Natural Science Foundation of Jiangsu Province (No. BK2010261);the Fundamental Research Funds for the Central Universities (No. 2011XZZX005),China

摘　　要：Recent finance and debt crises have made credit risk management one of the most important issues in financial research.Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics.In this paper,a novel feature-weighted support vector machine(SVM) credit scoring model is presented for credit risk assessment,in which an F-score is adopted for feature importance ranking.Considering the mutual interaction among modeling features,random forest is further introduced for relative feature importance measurement.These two feature-weighted versions of SVM are tested against the traditional SVM on two real-world datasets and the research results reveal the validity of the proposed method.Recent finance and debt crises have made credit risk management one of the most important issues in financial research.Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics.In this paper,a novel feature-weighted support vector machine（SVM） credit scoring model is presented for credit risk assessment,in which an F-score is adopted for feature importance ranking.Considering the mutual interaction among modeling features,random forest is further introduced for relative feature importance measurement.These two feature-weighted versions of SVM are tested against the traditional SVM on two real-world datasets and the research results reveal the validity of the proposed method.

关 键 词：Credit scoring model Support vector machine(SVM) Feature weight Random forest 

分 类 号：F830.5[经济管理—金融学] TP18[自动化与计算机技术—控制理论与控制工程]