Global Optimization of Support Vector Machines Using Genetic Algorithms for Bankruptcy Prediction

  • Hyunchul Ahn
  • Kichun Lee
  • Kyoung-jae Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)


One of the most important research issues in finance is building accurate corporate bankruptcy prediction models since they are essential for the risk management of financial institutions. Thus, researchers have applied various data-driven approaches to enhance prediction performance including statistical and artificial intelligence techniques. Recently, support vector machines (SVMs) are becoming popular because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. In addition, they don’t require huge training samples and have little possibility of overfitting. However, in order to use SVM, a user should determine several factors such as the parameters of a kernel function, appropriate feature subset, and proper instance subset by heuristics, which hinders accurate prediction results when using SVM. In this study, we propose a novel approach to enhance the prediction performance of SVM for the prediction of financial distress. Our suggestion is the simultaneous optimization of the feature selection and the instance selection as well as the parameters of a kernel function for SVM by using genetic algorithms (GAs). We apply our model to a real-world case. Experimental results show that the prediction accuracy of conventional SVM may be improved significantly by using our model.


Support Vector Machine Feature Selection Feature Subset Support Vector Machine Model Kernel Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Babu, T.R., Murty, M.N.: Comparison of genetic algorithm based prototype selection schemes. Pattern Recognition 34(2), 523–525 (2001)CrossRefGoogle Scholar
  2. 2.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001), Software available at
  3. 3.
    Drucker, H., Wu, D., Vapnik, V.N.: Support vector machines for spam categorization. IEEE Transactions on Neural Networks 10(5), 1048–1054 (1999)CrossRefGoogle Scholar
  4. 4.
    Fan, A., Palaniswami, M.: Selecting bankruptcy predictors using a support vector machine approach. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, pp. 354–359 (2000)Google Scholar
  5. 5.
    Fu, Y., Shen, R.: GA based CBR approach in Q&A system. Expert Systems with Applications 26(2), 167–170 (2004)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Han, J., Kamber, M.: Datamining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)Google Scholar
  7. 7.
    Harnett, D.L., Soni, A.K.: Statistical methods for business and economics. Addison-Wesley, Massachusetts (1991)Google Scholar
  8. 8.
    Howley, T., Madden, M.G.: The Genetic Kernel Support Vector Machine: Description and Evaluation. Artificial Intelligence Review 24(3-4), 379–395 (2005)CrossRefGoogle Scholar
  9. 9.
    Jack, L.B., Nandi, A.K.: Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms. Mechanical Systems and Signal Processing 16(2-3), 373–390 (2002)CrossRefGoogle Scholar
  10. 10.
    Kim, D.S., Nguyen, H.-N., Park, J.S.: Genetic algorithm to improve SVM based network intrusion detection system. In: Proceedings of the 19th International Conference on Advanced Information Networking and Applications, pp. 155–158 (2005)Google Scholar
  11. 11.
    Kim, K.: Financial forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003)CrossRefGoogle Scholar
  12. 12.
    Kim, K.: Toward global optimization of case-based reasoning systems for financial forecasting. Applied Intelligence 21(3), 239–249 (2004)CrossRefMATHGoogle Scholar
  13. 13.
    Kim, K.: Artificial neural networks with evolutionary instance selection for financial forecasting. Expert Systems with Applications 30(3), 519–526 (2006)CrossRefGoogle Scholar
  14. 14.
    Lee, K., Byun, H.: A New Face Authentication System for Memory-Constrained Devices. IEEE Transactions on Consumer Electronics 49(4), 1214–1222 (2003)CrossRefGoogle Scholar
  15. 15.
    Li, L., Tang, H., Wu, Z., Gong, J., Gruidl, M., Zou, J., Tockman, M., Clark, R.A.: Data mining techniques for cancer detection using serum proteomic profiling. Artificial Intelligence in Medicine 32(2), 71–83 (2004)CrossRefGoogle Scholar
  16. 16.
    Min, J.H., Lee, Y.-C.: Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications 28(4), 603–614 (2005)CrossRefGoogle Scholar
  17. 17.
    Min, S.-H., Lee, J., Han, I.: Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Systems with Applications (forthcoming, 2006)Google Scholar
  18. 18.
    Mukherjee, S., Osuna, E., Girosi, F.: Nonlinear prediction of chaotic time series using support vector machines. In: Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, pp. 511–520 (1997)Google Scholar
  19. 19.
    Pai, P.-F., Hong, W.-C.: Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms. Electric Power Systems Research 74(3), 417–425 (2005)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Reeves, C.R., Taylor, S.J.: Selection of training sets for neural networks by a genetic algorithm. In: Eiden, A.E., Back, T., Schoenauer, M., Schwefel, H.-P. (eds.) Parallel problem-solving from nature-PPSN V, Springer, Berlin (1998)Google Scholar
  21. 21.
    Samanta, B.: Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mechanical Systems and Signal Processing 18(3), 625–644 (2004)CrossRefMathSciNetGoogle Scholar
  22. 22.
    Shin, K.-S., Lee, T.S., Kim, H.-J.: An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications 28(1), 127–135 (2005)CrossRefGoogle Scholar
  23. 23.
    Sun, Z., Bebis, G., Miller, R.: Object detection using feature subset selection. Pattern Recognition 37(11), 2165–2176 (2004)CrossRefGoogle Scholar
  24. 24.
    Tay, F.E.H., Cao, L.: Application of support vector machines in financial time series forecasting. OMEGA: The International Journal of Management Science 29(4), 309–317 (2001)CrossRefGoogle Scholar
  25. 25.
    Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)MATHGoogle Scholar
  26. 26.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (2000)Google Scholar
  27. 27.
    Yu, E., Cho, S.: Keystroke dynamics identity verification: its problems and practical solutions. Computers & Security 23(5), 428–440 (2004)CrossRefGoogle Scholar
  28. 28.
    Yu, E., Cho, S.: Constructing response model using ensemble based on feature subset selection. Expert Systems with Applications 30(2), 352–360 (2006)CrossRefGoogle Scholar
  29. 29.
    Zhao, X.-M., Cheung, Y.-M., Huang, D.-S.: A novel approach to extracting features from motif content and protein composition for protein sequence classification. Neural Networks 18(8), 1019–1028 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hyunchul Ahn
    • 1
  • Kichun Lee
    • 2
  • Kyoung-jae Kim
    • 3
  1. 1.Graduate School of ManagementKorea Advanced Institute of Science and TechnologyDongdaemun-Gu, SeoulKorea
  2. 2.R&D Center, Samsung Networks Inc.Kangnam-Gu, SeoulKorea
  3. 3.Department of Management Information SystemsDongguk UniversityChung-Gu, SeoulKorea

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