Consumer credit scoring model based on SVM optimized by PSO

摘要：

Consumer credit scoring plays an important role for commercial banks to keep away from consumer credit risks. Aiming at the insufficiency of support vector machine (SVM) used in consumer credit scoring,this paper presents a method to optimize the parameters of SVM by using particle swarm optimization (PSO). Using a modified PSO to optimize the parameters of SVM and using the particles fitness to control the type Ⅱ error which brings huger loss to commercial banks in consumer credit scoring,compared with BP neural network,the application results indicate that the SVM model optimized by PSO gets higher classification accuracy with lower type Ⅱ error rate and the model shows stronger robustness,which presents more applicable for commercial banks to control consumer credit risks.

关键词： consumer credit scoring support vector machine particle swarm optimization

作者: Minghui Jiang Xuchuan Yuan

作者单位: School of Management,Harbin Institute of Technology,Harbin 150001,China

会议类型: 国际会议

会议名称: The First International Conference on Management Innovation(ICMI 2007)(管理创新会议)

会议地点: 上海

会议语种:英文

页码: 624-628

在线出版日期: 2007-06-04（万方平台首次上网日期，不代表论文的发表时间）