Fuzzy Support Vector Machine Based on Vague Sets for Credit Assessment

Credit assessment is one of important tools help financial institutions to hedge the credit risks. Support vector machine and fuzzy support vector machine have been applied in Credit assessment field. Fuzzy support vector machine (FSVM) does not treat equally the input point. It improves the generalization power of traditional SVM by applying a fuzzy membership to each input data point. In this paper, we propose a new FSVM based on vague sets that apply a truth-membership and a false- membership to each data point of training sets. In order to verity the effectiveness of the new FSVM, a real case of home loan data sets is given and the experimental results show that the model is promising.

[1]  David J. Hand,et al.  Statistical Classification Methods in Consumer Credit Scoring: a Review , 1997 .

[2]  Edward I. Altman,et al.  Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience) , 1994 .

[3]  Lipo Wang.  Support vector machines : theory and applications , 2005 .

[4]  W.-L. Gau,et al.  Vague sets , 1993, IEEE Trans. Syst. Man Cybern..

[5]  H. P. Huang,et al.  Fuzzy Support Vector Machines for Pattern Recognition and Data Mining , 2002 .

[6]  Soushan Wu,et al.  Credit rating analysis with support vector machines and neural networks: a market comparative study , 2004, Decis. Support Syst..

[7]  Daniel Martin,et al.  Early warning of bank failure: A logit regression approach , 1977 .

[8]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[9]  Sheng-De Wang,et al.  Fuzzy support vector machines , 2002, IEEE Trans. Neural Networks.

[10]  Kin Keung Lai,et al.  A new fuzzy support vector machine to evaluate credit risk , 2005, IEEE Transactions on Fuzzy Systems.

[11]  Peter J. Barry,et al.  Determining the Probability of Default and Risk-Rating Class for Loans in the Seventh Farm Credit District Portfolio , 2006 .

[12]  Edward I. Altman,et al.  FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .

[13]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[14]  Lipo Wang,et al.  Data Mining With Computational Intelligence , 2006, IEEE Transactions on Neural Networks.

[15]  Edward C. Lawrence,et al.  Forecasting losses on a liquidating long-term loan portfolio , 1995 .

[16]  Xuegong Zhang,et al.  Using class-center vectors to build support vector machines , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).