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A total least squares proximal support vector classifier for credit risk evaluation

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Abstract

In this paper, a total least squares (TLS) version of proximal support vector machines (PSVM) is proposed for credit risk evaluation. The formulation of this new model is different from the original PSVM model, so a novel iterative algorithm is proposed to solve this model. A simulation test is first implemented on a classic two-spiral dataset, and then an empirical experiment is conducted on two publicly available credit datasets. The experimental results show that the proposed total least squares PSVM (TLS-PSVM) is at least comparable with PSVM and better than other models including standard SVM model.

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References

  • Baesens B, Van Gestel T, Viaene S, Stepanova M, Suykens J, Vanthienen J (2004) Benchmarking state-of-art classification algorithms for credit scoring. J Oper Res Soc 54:627–635

    Article  Google Scholar 

  • Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861–874

    Article  Google Scholar 

  • Frank A, Asuncion A (2010) UCI machine learning repository, University of California Irvine. http://archive.ics.uci.edu/ml

  • Fu G, and Hu G (2008) Total least square support vector machine for regression. In: Proceedings of the 2008 international conference on intelligent computation technology and automation, vol 1, pp 271–275

  • Fung G, and Mangasarian OL (2001) Proximal support vector machine classifiers. In: Proceedings of international conference of knowledge discovery and data mining, pp 77–86

  • Golub G (1973) Some modified matrix eigenvalue problems. SIAM Rev 15:318–334

    Article  MathSciNet  MATH  Google Scholar 

  • Golub G, and Van Loan C (1980) An analysis of the total least squares problem. SIAM J Numer Anal 17:883–893

    Article  MathSciNet  MATH  Google Scholar 

  • Hardle W, Moro RA, Schafer D (2005) Predicting bankruptcy with support vector machines. In: Statistical tools for finance and insurance. Springer, Berlin, pp 225–248

  • Levenberg K (1944) A method for the solution of certain non-linear problems in least squares. Q Appl Math 2:164–168

    MathSciNet  MATH  Google Scholar 

  • Markovsky I, Van Huffel S (2007) Overview of total least-squares methods. Signal Process 87:2283–2302

    Article  MATH  Google Scholar 

  • Marquardt D (1963) An algorithm for least-squares estimation of nonlinear parameters. SIAM J Appl Math 11:431–441

    Article  MathSciNet  MATH  Google Scholar 

  • Mathworks (2008), MATLAB R2008a

  • Peng X, Wang Y (2009) A normal least squares support vector machine (NLS-SVM) and its learning algorithm. Neurocomputing 72:3734–3741

    Article  Google Scholar 

  • Rakotomamonjy A (2004) Optimizing area under ROC curve with SVMs, ECAI 04 ROC and Artificial Intelligence Workshop, 20–27 August, Valencia, Spain

  • Suykens JAK, Vandewalle JV (1999) Least squares support vector machine classifiers. Neural Process Lett 9:293–300

    Article  MathSciNet  Google Scholar 

  • Suykens JAK, van Gestel T, De Brabanter J, De Moor B, and Vandewalle JV (2002) Least squares support vector machines. World Scientific, Singapore

  • Vapnik V (1995) The nature of statistical learning theory. Springer, New York

  • Vapnik V (1998) Statistical learning theory. Wiley, New York

  • Van Gestel T, Suykens JAK, Lanckriet G, Lambrechts A, De Moor B, Vandewalle JV (2002) Bayesian framework for least squares support vector machine classifiers, Gaussian processes and kernel Fisher discriminant analysis. Neural comput 15:1115–1148

    Article  Google Scholar 

  • Van Gestel T, Baesens B, Garcia J, van Dijcke P (2003) A support vector machine approach to credit scoring. Bank en Financiewezen 2:73–82

    Google Scholar 

  • Yu L, Wang SY, Lai KK (2009) An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: the case of credit scoring. Eur J Oper Res 195:942–959

    Article  MathSciNet  MATH  Google Scholar 

  • Yu L, Yao X, Wang S, Lai KK (2011) Credit risk evaluation using a weighted least squares SVM classifier with design of experiment for parameter selection. Expert Syst Appl 38:15392–15399

    Article  Google Scholar 

  • Zhou LG, Lai KK (2009) Credit scoring models with AUC maximization based on weighted SVM. Int J Inf Technol Decis Mak 8(4):677–696

    Article  MATH  Google Scholar 

Download references

Acknowledgments

This work is partially supported by grants from the National Science Fund for Distinguished Young Scholars (NSFC No. 71025005) and the National Natural Science Foundation of China (NSFC No. 90924024).

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Authors and Affiliations

  1. School of Economics and Management, Beijing University of Chemical Technology, Beijing, 100029, China

    Lean Yu

  2. Alibaba Business College, Hangzhou Normal University, Hangzhou, 310036, China

    Lean Yu

  3. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Xiao Yao

  4. Business School, University of Edinburgh, Edinburgh, EH8 9JS, UK

    Xiao Yao

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  1. Lean Yu
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  2. Xiao Yao
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Correspondence to Lean Yu.

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Yu, L., Yao, X. A total least squares proximal support vector classifier for credit risk evaluation. Soft Comput 17, 643–650 (2013). https://doi.org/10.1007/s00500-012-0936-z

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  • DOI: https://doi.org/10.1007/s00500-012-0936-z

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