Paper Title:
To Optimize a BP Network System
  Abstract

The neural network has been introduced into the studies of credit risk assessment. However, the ratio of the dataset for training and testing is difficult to determine, so the neural network is not robust enough to give the judgment. Therefore, using the 2000 instances of personal consumer credit data set for approval of credit applications of a provincial-level China Construction Bank, for the BP neural network model, the study focused on the ratio of the dataset for training and testing. The results show that, when the ratio of the dataset for training and testing is 800:1200, the neural network model 2 for credit risk assessment has better performance. And it can achieve the desired accuracy and computational efficiency, so the BP network system for credit risk assessment is optimized.

  Info
Periodical
Edited by
Shaobo Zhong, Yimin Cheng and Xilong Qu
Pages
919-923
DOI
10.4028/www.scientific.net/AMM.50-51.919
Citation
W. Tong, L. P. Qin, "To Optimize a BP Network System", Applied Mechanics and Materials, Vols. 50-51, pp. 919-923, 2011
Online since
February 2011
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$32.00
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