| 基于主分量分析和BP神经网络的个人信用评估模型 |
| |
| 引用本文： | 姚尚锋.基于主分量分析和BP神经网络的个人信用评估模型[J].数学的实践与认识,2007,37(21):21-24. |
| |
| 作者姓名： | 姚尚锋 |
| |
| 作者单位： | 蚌埠坦克学院数理室,安徽,蚌埠,233013 |
| |
| 摘 要： | 运用基于主分量分析和神经网络(PCA-NN)的个人信用评估模型以期取得更好的预测分类能力.经实证分析及与SVM方法、线性判别分析、Logistic回归分析、最近邻估计、分类回归树及神经网络等方法的对比,结果表明,该方法有很好的预测效果.
|
| 关 键 词： | 主分量分析 神经网络 信用评估 |
| 修稿时间： | 2007年5月20日 |
Personal Credit Scoring Models on Principal Components Analysis and Neural Network |
| |
| YAO Shang-feng.Personal Credit Scoring Models on Principal Components Analysis and Neural Network[J].Mathematics in Practice and Theory,2007,37(21):21-24. |
| |
| Authors: | YAO Shang-feng |
| |
| Abstract: | This paper applies principal components analysis and neural network to the credit scoring prediction problem in an attempt to suggest a new model with better classification accuracy.To evaluate the prediction accuracy of the model,we compare its performance with those of linear disciminating analysis,logistic regression analysis,K-nearest neighbors,classification and regression tree and neural network.The experiment results show the model have a very good prediction accuracy. |
| |
| Keywords: | principal components analysis neural network credit scoring |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
|
