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Mathematical Problems in Engineering
Volume 2015, Article ID 197258, 7 pages
http://dx.doi.org/10.1155/2015/197258
Research Article

Construction and Application Research of Isomap-RVM Credit Assessment Model

School of Economics and Management, Wuhan University, Wuhan 430072, China

Received 5 November 2014; Accepted 10 January 2015

Academic Editor: Honglei Xu

Copyright © 2015 Guangrong Tong and Siwei Li. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Credit assessment is the basis and premise of credit risk management systems. Accurate and scientific credit assessment is of great significance to the operational decisions of shareholders, corporate creditors, and management. Building a good and reliable credit assessment model is key to credit assessment. Traditional credit assessment models are constructed using the support vector machine (SVM) combined with certain traditional dimensionality reduction algorithms. When constructing such a model, the dimensionality reduction algorithms are first applied to reduce the dimensions of the samples, so as to prevent the correlation of the samples’ characteristic index from being too high. Then, machine learning of the samples will be conducted using the SVM, in order to carry out classification assessment. To further improve the accuracy of credit assessment methods, this paper has introduced more cutting-edge algorithms, applied isometric feature mapping (Isomap) for dimensionality reduction, and used the relevance vector machine (RVM) for credit classification. It has constructed an Isomap-RVM model and used it to conduct financial analysis of China's listed companies. The empirical analysis shows that the credit assessment accuracy of the Isomap-RVM model is significantly higher than that of the Isomap-SVM model and slightly higher than that of the PCA-RVM model. It can correctly identify the credit risks of listed companies.