贝叶斯网络个人信用评估模型被引量：19

Customer Credit Scoring Models on Bayesian Network Classification

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摘要研究了朴素贝叶斯分类器、树增强朴素贝叶斯分类器2种贝叶斯网络信用评估模型的精度,用10层交叉验证在2个真实数据集上对贝叶斯网络信用评分模型进行了测试并与神经网络模型进行了比较。结果表明,贝叶斯网络信用评估模型具有较高的分类精度,在信用评估中具有优势。 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.

作者郭春香李旭升

机构地区四川大学工商管理学院西南交通大学经济管理学院

出处《系统管理学报》北大核心 2009年第3期249-254,260,共7页 Journal of Systems & Management

基金国家自然科学基金资助项目(70771093) 四川省教育厅科研项目(2006C082)

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 credit scoring Bayesian network naive Bayesian tree augmented naive Bayesian neural（network）

分类号 F830.5 [经济管理—金融学]

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参考文献16

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