Journal of information and communication convergence engineering

  • 제8권4호
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  • Pages.370-376
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  • 2010
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  • 2234-8255(pISSN)
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  • 2234-8883(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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Optimizing SVM Ensembles Using Genetic Algorithms in Bankruptcy Prediction

  • 투고 : 2010.07.16
  • 심사 : 2010.07.28
  • 발행 : 2010.08.31
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⟨ 이전 논문 다음 논문 ⟩

초록

Ensemble learning is a method for improving the performance of classification and prediction algorithms. However, its performance can be degraded due to multicollinearity problem where multiple classifiers of an ensemble are highly correlated with. This paper proposes genetic algorithm-based optimization techniques of SVM ensemble to solve multicollinearity problem. Empirical results with bankruptcy prediction on Korea firms indicate that the proposed optimization techniques can improve the performance of SVM ensemble.

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참고문헌

  1. Meyer, P. A., & Pifer, H. (1970). Prediction of bank failures. The Journal of Finance, 25, 853-868. https://doi.org/10.2307/2325421
  2. Altman, E. L. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. https://doi.org/10.2307/2978933
  3. Altman, E. L., Edward, I., Haldeman, R., & Narayanan, P. (1977). A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1, 29-54. https://doi.org/10.1016/0378-4266(77)90017-6
  4. Dimitras, A. I., Zanakis, S. H., & Zopounidis, C. (1996). A survey of business failure with an emphasis on prediction methods and industrial applications. European Journal of Operational Research, 90(3), 487-513. https://doi.org/10.1016/0377-2217(95)00070-4
  5. Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131. https://doi.org/10.2307/2490395
  6. Pantalone, C., & Platt, M. B. (1987). Predicting commercial bank failure since deregulation. New England Economic Review, 37-47.
  7. Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22(1), 59-82. https://doi.org/10.2307/2490859
  8. Han, I., Chandler, J. S., & Liang, T. P. (1996). The impact of measurement scale and correlation structure on classification performance of inductive learning and statistical methods. Expert System with Applications, 10(2), 209-221. https://doi.org/10.1016/0957-4174(95)00047-X
  9. Shaw, M., & Gentry, J. (1998). Using and expert system with inductive learning to evaluate business loans. Financial Management, 17(3), 45-56.
  10. Bryant, S. M. (1997). A case-based reasoning approach to bankruptcy prediction modeling. International Journal of Intelligent Systems in Accounting, Finance and Management, 6(3), 195-214
  11. Buta, P. (1994). Mining for financial knowledge with CBR. AI Expert, 9(10), 34-41.
  12. Laitinen, T., & Kankaanpaa, M. (1999) Comparative analysis of failure prediction methods: the Finish case. European Accounting Review, 8(1), 67-92. https://doi.org/10.1080/096381899336159
  13. Odom, M., & Sharda, R. (1990). A neural network for bankruptcy prediction, Proceedings of the International Joint Conference on Neural Networks, IEEE Press, San Diego, CA.
  14. Ravi, P., & Ravi, K. V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques-a review. European Journal of Operational Research, 180, 1-28. https://doi.org/10.1016/j.ejor.2006.08.043
  15. Min, S. H.; Lee, J. M., & Han, I. G. (2006). Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Systems with Applications, 31, 652-660. https://doi.org/10.1016/j.eswa.2005.09.070
  16. Shin, K., Lee, T., & Kim, H. (2005). An application of support vector machines in bankruptcy prediction. Expert Systems with Applications, 28, 127-135. https://doi.org/10.1016/j.eswa.2004.08.009
  17. Alfaro, E., Gamez, M., & Garcia, N. (2007). Multiclass corporate failure prediction by AdaBoost.M1. Advanced Economic Research, 13, 301-312. https://doi.org/10.1007/s11294-007-9090-2
  18. Alfaro, E., García, N., Gámez, M., & Elizondo, D. (2008). Bankruptcy forecasting: an empirical comparison of AdaBooost and neural networks. Decision Support Systems, 45, 110-122. https://doi.org/10.1016/j.dss.2007.12.002
  19. Kim, M. J., (2009). A Performance Comparison of Ensembles in Bankruptcy Prediction, Entrue Journal of Information Technology, 8(2), 41-49.
  20. Perrone, M. E. (1994). Putting it all together: Methods for combining neural networks. In J. D. Cowan, G. Tesauro, & J. Alspector, (Eds.), Advances in Neural Information Processing Systems, 6, (pp. 1188-1189). San Mateo, CA: Morgan Kaufmann.
  21. Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227.
  22. Buciu, I., Kotrooulos, C., & Pitas, I. (2001). Combining support vector machines for accuracy face detection, Proc. ICIP, 1054-1057.
  23. Dong, Y. S., & Han, K. S. (2004). A comparison of several ensemble methods for text categorization, IEEE International Conference on Service Computing.
  24. Eom, J. H., Kim, S. C. & Zhang, B. T. (in press). AdaaCDSS-E: A classifier ensemble based clinical decision support systems for cardiovascular disease level prediction, Expert Systems with Applications.
  25. Valentini, G., Muselli, M. & Ruffino, F. (2003). Bagged ensembles of SVMs or gene expression data analysis, The IEEE-INNS-ENNS International Joint Conference on Neural Networks, 1844-1849
  26. Breiman, L. (1994). Bagging predictors. Machine Learning, 24(2), 123-140.
  27. Freund, Y. (1995). Boosting a weak learning algorithm by majority. Information and Computation, 121(2), 256-285. https://doi.org/10.1006/inco.1995.1136
  28. Banfield, R. E., Hall, L. O., Bowyer, K. W., & Kegelmeyer, W. P. (2007). A comparison of decision tree ensemble creation techniques. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(1), 173-180. https://doi.org/10.1109/TPAMI.2007.250609
  29. Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36, 105-139. https://doi.org/10.1023/A:1007515423169
  30. Drucker, H., & Cortes, C. (1996). Boosting decision trees, Advanced Neural Information Processing Systems, 8.
  31. Quinlan, J. R. (1996). Bagging, boosting and C4.5. Machine Learning: Proceedings of the Fourteenth International Conference , 725-730).
  32. Valentini, G., & Dietterich, T. (2004). Bias-variance analysis of support vector machines for the development of SVM-based ensemble methods, Journal of Machine Learning Research, 5, 725-775.
  33. Valentini, G. (2005). An experimental bias-variance analysis of SVM ensembles based on resampling techniques. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, 35(6), 1252-1271. https://doi.org/10.1109/TSMCB.2005.850183
  34. Pang, S., Kim, D., & Sung, Y. (2003). Membership authentication in the dynamic group by face classification using SVM ensemble, Pattern Recognition Letter, 24, 215-225. https://doi.org/10.1016/S0167-8655(02)00213-1
  35. Pang, S., Kim, D., & Sung, Y. (2003). Membership authentication in the dynamic group by face classification using SVM ensemble, Pattern Recognition Letter, 24, 215-225. https://doi.org/10.1016/S0167-8655(02)00213-1
  36. Gordon, J. J., Towsey, M. W., Hogan, J. M., Mathews S. A., & Timms, P. (2006). Improved prediction of bacterial transcription start sites, Bioinformatics, 22(2) 142-148 https://doi.org/10.1093/bioinformatics/bti771
  37. Hu, Q., He, Z., Zhang, Z., and Zi, Y. (2007). Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble. Mechanical Systems and Signal Processing, 21, 688-705. https://doi.org/10.1016/j.ymssp.2006.01.007
  38. Lei, Z, Yang, Y., & Wu, Z.(2006). Ensemble of support vector machine for text-independent speaker recognition, International Journal of Computer Science and Network Security 6(5A) 163-167.
  39. Wang, Y. Q. (2008). Building credit scoring systems based on support-based support vector machine ensemble, Fourth International Conference on Natural Computation, 323-326.
  40. Hansen, L., & Salamon, P. (1990). Neural network ensembles, IEEE Trans. PAMI, Vol. 12, 993-1001. https://doi.org/10.1109/34.58871
  41. Ho, T. K. (2002). Multiple classifier combination: lessons and next steps, in Hybrid Methods in Patter Recognition. (Ed. By H. Bubke & A. Kandel), World Scientific, 2002.
  42. Zhou, Z. H. Wu, J. X., & Tang, W. (2002). Ensembling neural networks: many could better than all, Artificial Intelligence, 137, 239-263. https://doi.org/10.1016/S0004-3702(02)00190-X
  43. Oliveira, L. S., Sabourin, R., Bortolozzi, F., & Suen, C.Y. (2003). Feature selection for ensembles: a hierarchical multi-objective genetic algorithm approach, ICDAR 2003.
  44. Optiz, D., & Maclin, R. (1999). Popular ensemble methods: an empirical study. Journal of Artificial Intelligence, 11, 169-198.
  45. Maia, T. T., Braga, A. P., & Carvalho, A. F. (2008). Hybrid classification algorithms based on boosting and support vector machines, Kybernetes, 37(9), 1469-1491 https://doi.org/10.1108/03684920810907814

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