To Optimize a BP Network System

Abstract:

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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 and 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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$35.00

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