Application of Particle Swarm Optimization to Credit Risk Assessment

摘要：

In this paper, we apply particle swarm optimization (PSO) to solve feature subset selection problems. The proposed PSO algorithm is combined with nearest neighbor classifiers. The algorithm is applied to classify credit risk using benchmark data sets from UCI databases. The experimental results presented in the paper demonstrate that the application of our proposed method lets to achieve better results than the existing methods in terms of solution quality and computational efficiency.

作者: Cheng-An Li Jing Xu He-Yong Wang

作者单位: Department of E-Business,South China University of Technology,Guangzhou,Guangdong,P. R. China,510006 Department of E-Business,South China University Technology,Guangzhou,Guangdong,P. R. China,510006

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Information and Automation(2009年 IEEE信息与自动化国际学术会议)

会议地点: 珠海、澳门

会议语种:英文

页码: 1427-1432

在线出版日期: 2009-06-22（万方平台首次上网日期，不代表论文的发表时间）