Management Science and Financial Engineering

  • 제17권1호
  • /
  • Pages.19-43
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  • 2011
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  • 2287-2043(pISSN)
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  • 2287-2361(eISSN)
한국경영과학회 (The Korean Operations Research and Management Science Society)
권호 표지

Tuning the Architecture of Support Vector Machine: The Case of Bankruptcy Prediction

  • 투고 : 2010.09.29
  • 심사 : 2010.12.06
  • 발행 : 2011.05.31
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⟨ 이전 논문 다음 논문 ⟩

초록

Tuning the architecture of SVM (support vector machine) is to build an SVM model of better performance. Two different tuning methods of the grid search and the GA (genetic algorithm) have been addressed in the literature, each of which has its own methodological pros and cons. This paper suggests a combined method for tuning the architecture of SVM models, which employs the GAM (generalized additive models), the grid search, and the GA in sequence. The GAM is used for selecting input variables, and the grid search and the GA are employed for finding optimal parameter values of the SVM models. Applying the method to a bankruptcy prediction problem, we show that SVM model tuned by the proposed method outperforms other SVM models.

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참고문헌

  1. Boser, B. E., I. M. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifiers," Proceedings of the 5th annual ACM workshop on computational learning theory, New York, NY: ACM Press, 1992.
  2. Campbell, C., "Kernel methods: a survey of current techniques," Neurocomputing 48 (2002), 63-84. https://doi.org/10.1016/S0925-2312(01)00643-9
  3. Cao, L. J., "Support vector machines experts for time series forecasting," Neurocomputing 51 (2003), 321-339. https://doi.org/10.1016/S0925-2312(02)00577-5
  4. Duan, K. and S. S. Keerthi, "Evaluation of simple performance measures for tuning SVM hyperparameters," Neurocomputing 51 (2003), 41-59. https://doi.org/10.1016/S0925-2312(02)00601-X
  5. Fan, A. and M. Palaniswami, "A new approach to corporate loan default prediction from financial statements," Proceedings of Computational Finance/ Forecasting Financial Markets Conference (CF/FFM‐2000), London, 2000.
  6. Gestel, T. V., J. A. K. Suykens, D. E. Baestaens, A. Lambrechts, G. Lanckriet, B. Vandaele, B. De Moor, and J. Vandewalle, "Financial time series prediction using least squares support vector machines within the evidence framework," IEEE Transactions on Neural Networks 12 (2001), 809-821. https://doi.org/10.1109/72.935093
  7. Hastie, T. and R. Tibshirani, "Generalized additive models," Statistical Science 1 (1986), 297-318. https://doi.org/10.1214/ss/1177013604
  8. Hastie, T. and R. Tibshirani, Generalized additive models. London: Chapman and Hall, 1990.
  9. Howley, T. and M. G. Madden, "The genetic kernel support vector machine: Description and evaluation," Artificial Intelligence Review 24 (2005), 379-395. https://doi.org/10.1007/s10462-005-9009-3
  10. Huang, Z., H. Chen, C. Hsu, W. Chen, and S. Wu, "Credit rating analysis with support vector machines and neural networks: A market comparative study," Decision Support Systems 37 (2004), 543-55. https://doi.org/10.1016/S0167-9236(03)00086-1
  11. Kecman, V., Learning and soft computing, Cambridge, MA: The MIT Press, 2001.
  12. Kim, H. S. and S. Y. Sohn, "Support vector machines for default prediction of SMEs based on technology credit," European Journal of Operational Research 201 (2010), 838-846. https://doi.org/10.1016/j.ejor.2009.03.036
  13. Kim, K. J., "Financial time series forecasting using support vector machines," Neurocomputing 55 (2003), 307-320. https://doi.org/10.1016/S0925-2312(03)00372-2
  14. Min, J. H. and Y. C. Lee, "Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters," Expert Systems with Applications 28 (2005), 603-614. https://doi.org/10.1016/j.eswa.2004.12.008
  15. Pai, P.‐F. and W.‐C. Hong, "Support vector machines with simulated annealing algorithms in electricity load forecasting," Energy Conversion and Management 46 (2005), 2669-2688. https://doi.org/10.1016/j.enconman.2005.02.004
  16. Pendharkar, P., "A threshold‐varying artificial neural network approach for classification and its application to bankruptcy prediction problem," Computers & Operations Research 32 (2005), 2561-2582. https://doi.org/10.1016/j.cor.2004.06.023
  17. Piramuthu, S., H. Ragavan, and M. J. Shaw, "Using feature construction to improve performance of neural networks," Management Science 44 (1998), 416-430. https://doi.org/10.1287/mnsc.44.3.416
  18. Ruppert, V., M. P. Wand, and R. J. Carroll, Semiparametric regression, New York: Cambridge Press, 2003.
  19. Sancho, S., D. Mario, S. M. Jesus, P. Fernando, and B. Carlos, "Feature selection methods involving support vector machines for prediction of insolvency in non‐life insurance companies," International Journal of Intelligent Systems in Accounting, Finance and Management 12 (2004), 261-281. https://doi.org/10.1002/isaf.255
  20. Shin, K. S., T. S. Lee, and H. J. Kim, "An application of support vector machines in bankruptcy prediction model," Expert Systems with Applications 28 (2005), 127-135. https://doi.org/10.1016/j.eswa.2004.08.009
  21. Tay, F. E. H. and L. Cao, "Application of support vector machines in financial time series forecasting," OMEGA: The International Journal of Management Science 29 (2001), 309-317. https://doi.org/10.1016/S0305-0483(01)00026-3
  22. Vapnik, V. N., The Nature of Statistical Learning Theory, Verlag: Springer, 1995.
  23. Vapnik, V. N., Statistical Learning Theory, New York: Wiley, 1998.
  24. Viaene, S., R. A. Derrig, B. Baesens, and G. Dedene, "A comparison of state‐ofthe-art classification techniques for expert automobile insurance claim fraud detection," The Journal of Risk and Insurance 69 (2002), 373-421. https://doi.org/10.1111/1539-6975.00023
  25. Wu, C.‐H., G.‐H. Tzeng, Y.‐J. Goo, and W.‐C. Fang, "A real‐valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy," Expert Systems with Applications 32 (2007), 397-408. https://doi.org/10.1016/j.eswa.2005.12.008
  26. Yoon, J. S. and Y. S. Kwon, "A practical approach to bankruptcy prediction for small businesses: Substituting the unavailable financial data for credit card sales information," Expert Systems with Applications 37 (2010), 3624-3629. https://doi.org/10.1016/j.eswa.2009.10.029
  27. Zhang, G., M. Hu, B. Patuwo, and D. Indro, "Artificial neural networks in bankruptcy prediction: General framework and cross validation analysis," European Journal of Operational Research 116 (1999), 16-32. https://doi.org/10.1016/S0377-2217(98)00051-4