An Improved Support Vector Machine for Credit Scoring


Article Preview

With the development of Chinas economy, credit scoring has become important. The general credit scoring model is to solve the two classification problems, but in real life we often encounter multiple classification problems. This paper proposes a multi-class support vector machine based on genetic algorithm, which can solve multiple classification problems in the behavior assessment model.



Edited by:

D.L. Liu, X.B. Zhu, K.L. Xu and D.M. Fang




B. Tang and S. B. Qiu, "An Improved Support Vector Machine for Credit Scoring", Applied Mechanics and Materials, Vols. 513-517, pp. 4407-4410, 2014

Online since:

February 2014





* - Corresponding Author

[1] Thomas L C, Edelman D B, Crook J N. Credit Scoring and Its Application[M]. Society for Industrial and Applied Mathematics, Philadelphia, (2002).

[2] Davis, R. H., Edelman, D.B., Gamerman.J. Machine learning algorithms for credit-card applications[J]. Journal of Mathematics Applied in Business and Industry, 1992, 4: 43-51.

[3] Fogarty TC, Ireson N. Evolving Bayesian classifiers for credit control: a comparison with other machine learning methods[J]. IMA Journal of Mathematics Applied in Business and Industry, 1994. 5: 63-75.

DOI: 10.1093/imaman/5.1.63

[4] Shin K S, Lee T S, Kim H J. An Application of Support Vector Machines in Bankruptcy Prediction Model [J]. Expert Systems with Applications, 2005, 28.

DOI: 10.1016/j.eswa.2004.08.009

[5] V. Vapnik. Statistical Learning Theory[M]. New York: John Wiley & Sons, (1998).

[6] J.A.K. Suykens, J. Vandewalle: Least Squares Support Vector Machine Classifiers. Neural Processing Letters 9(3) (1999) 293-300.

[7] David A. Coley. An Introduction To Genetic Algorithms For Scientists And Engineers[M]. World Scientific Press, (1999).

[8] Hsu C W, Lin C J. A comparison of methods for multi-class support vector machines[J]. IEEE Transaction on Neural Networks, 2002, 13: 415-425.

[9] Chih-Wei Hsu and C.J. Lin. A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks 2002, 13(2): 415-425.

DOI: 10.1109/72.991427

[10] X U P, CHAN A K . Support vector machine for multi-class signal classification with unbalanced samples[A] Prceeding of the International Joint Conference on Neural Networks 2003, Portland: IEEE, 2003: 1116-1119.

DOI: 10.1109/ijcnn.2003.1223847

[11] V. Blanz, V. Vapnik and C. Burges. Multi-class discrimination with an extended support vector machine. Talk given at AT&T Bell Labs, (1995).

[12] Y. Lee, Y. Lin and G. Wahba. Multi-category support vector machines. Technical Report 1040, Department of Statistics, University of Madison, Wisconsin, (2001).

[13] J. Weston and C. Watkins. Multi-class support vector machines. In: Proceedings ESANN, Brussels, (1999).

Fetching data from Crossref.
This may take some time to load.