中文摘要：

个人信用评估是金融与银行界研究的重要内容.论文研究了三种朴素贝叶斯分类器信用评估模型的精度.在两个真实数据集上用10层交叉验证对朴素贝叶斯信用评估模型进行了测试,并与五种David West的神经网络个人信用评估模型进行了对比.结果表明朴素贝叶斯分类器具有较低的分类误差,在信用评估中有优势.

英文摘要：

Personal credit scoring plays an important role in financial and banking industry.This paper investigates the credit scoring accuracy of three naive Bayesian classifier models.They are tested using 10-fold cross validation with two real world data sets,and compared with five neural network models of David West＇s.Results demonstrate that the naive Bayesian classifiers are competitive with neural network classifiers and predominant in credit scoring domain.