A Credit Scoring Model based on Bayesian Network and Mutual Information

摘要：

　　Credit scoring profiles the client relationships of empirical attributes (variables) and leverages a scoring model to draw clients credibility.However, empirical attributes often contains a certain degree of uncertainty and requires feature selection.Bayesian network (BN) is an important tool for dealing with uncertain problems and information, Mutual information (MI) measures dependencies between random variables and is therefore a suitable feature selection technique for evaluating the relationship between variables in a complex classification tasks.Using Bayesian network as a statistical model, this study leverages mutual information to build a credit scoring model called BNMI.The learned Bayesian network structure is adaptively adjusted according to mutual information.Empirical study compared the results of BNMI with three existing baseline models.The results show that the proposed model outperforms the baseline models in terms of receiver operating characteristic (ROC), indicating promising application of our BNMI in the credit scoring area.

关键词： credit scoring model Bayesian network mutual information machine learning clients credibility

作者: Yuanhang Zhuang Zhuoming Xu Yan Tang

作者单位: College of Computer and Information Hohai University Nanjing, China

会议类型: 国际会议

会议名称: The 12th Web Information System and Application Conference第十二届全国Web信息系统及其应用学术会议(WISA2015)、全国第十次语义Web 与本体论学术研讨会(SWON2015)、全国第九次电子政务技术及应用学术研讨会(EGTA2015)

会议地点: 济南

会议语种:英文

页码: 281-286

在线出版日期: 2015-09-11（万方平台首次上网日期，不代表论文的发表时间）