摘要
银行贷款风险评估一直是金融界高度关注的主要问题,现有方法主要包括K-means聚类、BP神经网络、简单决策树、VAR方法等多种风险评估算法。但对于客户属性值缺失的案例,上述方法就很难达到良好的效果。为了解决属性值缺失的风险评估问题。提出了一种基于贝叶斯决策树算法的贷款风险评估算法(DBT),实验结果证明了该算法的有效性。
The risk assessment of bank loan has been valued highly by financial world .Various risk evaluation methods for risk classification ,such as K means clustering algorithm ,BP neural net-work ,simple decision tree and VAR ,have been produced ,but they all suffer the problem of attribute values missing .To solve this problem ,we propose a novel Bayesian decision tree algorithm （DBT ） , and the experimental results show its effectiveness .
出处
《广西师范学院学报（自然科学版）》
2014年第2期62-66,共5页
Journal of Guangxi Teachers Education University(Natural Science Edition)
基金
广西科技开发项目资助(桂科攻1348015-4)
广西教育厅科研项目资助(2011022D020)
