系统管理学报2009，Vol.18Issue(3)：249-254,260,7.
贝叶斯网络个人信用评估模型
Customer Credit Scoring Models on Bayesian Network Classification
摘要
Abstract
This paper investigates the credit scoring accuracy of two Bayesian network models: naive Bayesian and tree augmented naive Bayesian. They are tested using 10-fold cross validation with two real world data sets, and compared with neural network models. Results demonstrate that the Bayesian network credit scoring models are competitive with neural network models and predominant in credit scoring domain.关键词
信用评估/贝叶斯网络/朴素贝叶斯分类模型/树增强贝叶斯分类模型/神经网络Key words
credit scoring/Bayesian network/naive Bayesian/tree augmented naive Bayesian/neural network分类
管理科学引用本文复制引用
郭春香,李旭升..贝叶斯网络个人信用评估模型[J].系统管理学报,2009,18(3):249-254,260,7.基金项目
国家自然科学基金资助项目(70771093) （70771093）
四川省教育厅科研项目(2006C082) （2006C082）
