An application of a hybrid GA-SVM model to the credit scoring

摘要：

A robust credit scoring model is badly in demand and is of great significance in theprocess of constructing a credit system of our country. Since the optimal parameters search of SVM plays a crucial role in building an efficient classification model, we use a genetic algorithm to optimize the parameters of SVM for credit scoring. Experimental results reveal that the GA-SVM model achieved higher accuracy than that of other exiting classifiers, such as statistical methods, back-propagation neural network. The GA-SVM model is proved to be effective in searching the optimal parameters of SVM. The proposed hybrid system has a potential for credit scoring in terms of prediction accuracy and generalization ability.

关键词： Support vector machine Genetic algorithm credit scoring.

作者: Hong Tao Xinping Song

作者单位: School of Economy and Management Shaoxing university Shaoxing, P. R. China School of Business and Management,Donghua University, Shanghai, P. R. China

会议类型: 国际会议

会议名称: 2007 Conference on Systems Science, Management Science and System Dynamics(第二届系统科学、管理科学与系统动力学国际会议)

会议地点: 上海

会议语种:英文

页码: 1983-1988

在线出版日期: 2007-10-19（万方平台首次上网日期，不代表论文的发表时间）