Abstract
Credit risk is paid more and more attention by financial institutions, and credit scoring has become an active research topic. This paper proposes a new credit scoring method based on kernel matching pursuit (KMP). KMP appends sequentially basic functions from a kernel-based dictionary to an initial empty basis using a greedy optimization algorithm, to approximate a given function, and obtain the final solution with a linear combination of chosen functions. An outstanding advantage of KMP in solving classification problems is the sparsity of its solution. Experiments based on two real data sets from UCI repository show the effectiveness and sparsity of KMP in building credit scoring model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Myers, J.H., Forgy, E.W.: The development of numerical credit evaluation systems. Journal of the American Statistical Association 58(303), 799–806 (1963)
Wiginton, J.C.: A note on the comparison of logit and discriminant models of consumer credit behavior. Journal of Financial Quantitative Analysis 15(3), 757–770 (1980)
Zhou, X.Y., Zhang, D.F., Jiang, Y.: A new credit scoring method based on rough sets and decision tree. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 1081–1089. Springer, Heidelberg (2008)
West, D.: Neural network credit scoring models. Computers and Operations Research 27(11-12), 1131–1152 (2000)
Abdou, H.A.: Genetic programming for credit scoring: The case of Egyptian public sector banks. Expert Systems with Applications 36(9), 11402–11417 (2009)
Chuang, C.L., Lin, R.H.: Constructing a reassigning credit scoring model. Expert Systems with Applications 36(2), 1685–1694 (2009)
Zhou, L., Lai, K.K., Yu, L.: Credit scoring using support vector machines with direct search for parameters selection. Soft Comput. 13, 149–155 (2009)
Vincent, P., Bengio, Y.: Kernel Matching Pursuit. Machine Learning 48, 165–187 (2002)
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. School of Information and Computer Science, University of California(2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, J., Wei, H., Kong, C., Hou, X., Li, H. (2013). Credit Scoring Based on Kernel Matching Pursuit. In: Huang, DS., Gupta, P., Wang, L., Gromiha, M. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2013. Communications in Computer and Information Science, vol 375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39678-6_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-39678-6_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39677-9
Online ISBN: 978-3-642-39678-6
eBook Packages: Computer ScienceComputer Science (R0)