Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 302624, 20 pages
Research Article

A Corporate Credit Rating Model Using Support Vector Domain Combined with Fuzzy Clustering Algorithm

School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an 710049, China

Received 11 February 2012; Revised 19 April 2012; Accepted 9 May 2012

Academic Editor: Wanquan Liu

Copyright © 2012 Xuesong Guo et al. 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.


Corporate credit-rating prediction using statistical and artificial intelligence techniques has received considerable attentions in the literature. Different from the thoughts of various techniques for adopting support vector machines as binary classifiers originally, a new method, based on support vector domain combined with fuzzy clustering algorithm for multiclassification, is proposed in the paper to accomplish corporate credit rating. By data preprocessing using fuzzy clustering algorithm, only the boundary data points are selected as training samples to accomplish support vector domain specification to reduce computational cost and also achieve better performance. To validate the proposed methodology, real-world cases are used for experiments, with results compared with conventional multiclassification support vector machine approaches and other artificial intelligence techniques. The results show that the proposed model improves the performance of corporate credit-rating with less computational consumption.