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Development of Intelligent Credit Rating System using Support Vector Machines  

Kim Kyoung-jae (동국대학교 경영대학 정보관리학과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.9, no.7, 2005 , pp. 1569-1574 More about this Journal
Abstract
In this paper, I propose an intelligent credit rating system using a bankruptcy prediction model based on support vector machines (SVMs). SVMs are promising methods because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. This study examines the feasibility of applying SVM in Predicting corporate bankruptcies by comparing it with other data mining techniques. In addition. this study presents architecture and prototype of intelligeht credit rating systems based on SVM models.
Keywords
Support vector machines; Credit rating; Data mining;
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1 S. Mukherjee, E. Osuna, and F. Girosi, 'Nonlinear prediction of chaotic time series using support vector machines', in: Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, Amelia Island, FL, pp. 511-520, 1997
2 C.-C. Chang and C.-J. Lin, 'LIBSVM: a library for support vector machines', Technical Report, Department of Computer Science and Information Engineering. National Taiwan University, Available at http://www.csie.edue.tw/~chlin/papers/libsvm.pdf, 2001
3 K.S. Shin, T.S. Lee, and H. Kim, 'An application of support vector machines in bankruptcy prediction model', Expert Systems with Applications, Vol. 28, No. 1, pp. 127-135, 2005   DOI   ScienceOn
4 I.H. Witten, and E. Frank, 'Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations', Morgan Kaufmann Publishers, San Francisco, CA, 2000
5 K. Kim, 'Financial time series forecasting using support vector machines', Neurocomputing, Vol. 55, No. 1-2, pp. 307-319, 2003   DOI   ScienceOn
6 F.E.H. Tay and L. Cao, 'Application of support vector machines in financial time series forecasting', Omega, Vol. 29, pp. 309-317, 2001   DOI   ScienceOn
7 V.N. Vapnik, 'Statistical Learning Theory', Wiley, New York, 1998
8 H. Drucker, D. Wu, and V.N. Vapnik, 'Support vector machines for spam categorization', IEEE Transactions on Neural Networks, Vol. 10, No. 5, pp. 1048-1054, 1999   DOI   ScienceOn