Journal of Korea Society of Industrial Information Systems (한국산업정보학회논문지)

Korea Society of Industrial Information Systems (한국산업정보학회)
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Developing an Ensemble Classifier for Bankruptcy Prediction

부도 예측을 위한 앙상블 분류기 개발

  • Published : 2012.12.30
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Abstract

An ensemble of classifiers is to employ a set of individually trained classifiers and combine their predictions. It has been found that in most cases the ensembles produce more accurate predictions than the base classifiers. Combining outputs from multiple classifiers, known as ensemble learning, is one of the standard and most important techniques for improving classification accuracy in machine learning. An ensemble of classifiers is efficient only if the individual classifiers make decisions as diverse as possible. Bagging is the most popular method of ensemble learning to generate a diverse set of classifiers. Diversity in bagging is obtained by using different training sets. The different training data subsets are randomly drawn with replacement from the entire training dataset. The random subspace method is an ensemble construction technique using different attribute subsets. In the random subspace, the training dataset is also modified as in bagging. However, this modification is performed in the feature space. Bagging and random subspace are quite well known and popular ensemble algorithms. However, few studies have dealt with the integration of bagging and random subspace using SVM Classifiers, though there is a great potential for useful applications in this area. The focus of this paper is to propose methods for improving SVM performance using hybrid ensemble strategy for bankruptcy prediction. This paper applies the proposed ensemble model to the bankruptcy prediction problem using a real data set from Korean companies.

분류기의 앙상블 학습은 여러 개의 서로 다른 분류기들의 조합을 통해 만들어진다. 앙상블 학습은 기계학습 분야에서 많은 관심을 끌고 있는 중요한 연구주제이며 대부분의 경우에 있어서 앙상블 모형은 개별 기저 분류기보다 더 좋은 성과를 내는 것으로 알려져 있다. 본 연구는 부도 예측 모형의 성능개선에 관한 연구이다. 이를 위해 본 연구에서는 단일 모형으로 그 우수성을 인정받고 있는 SVM을 기저 분류기로 사용하는 앙상블 모형에 대해 고찰하였다. SVM 모형의 성능 개선을 위해 bagging과 random subspace 모형을 부도 예측 문제에 적용해 보았으며 bagging 모형과 random subspace 모형의 성과 개선을 위해 bagging과 random subspace의 통합 모형을 제안하였다. 제안한 모형의 성과를 검증하기 위해 실제 기업의 부도 예측 데이터를 사용하여 실험하였고, 실험 결과 본 연구에서 제안한 새로운 형태의 통합 모형이 가장 좋은 성과를 보임을 알 수 있었다.

Keywords

References

  1. Beaver, W, "Financial ratios as predictors of failure, empirical research in accounting: Selected studied", Journal of Accounting Research, 1966, pp.71-111.
  2. Altman, E. L, "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy", The Journal of Finance, 23(3), 1968, pp.589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00841.x
  3. Altman, E. L., Edward, I., Haldeman, R., & Narayanan, P. A, "new model to identify bankruptcy risk of corporations", Journal of Banking and Finance, 1, 1977, pp.29-54. https://doi.org/10.1016/0378-4266(77)90017-6
  4. Meyer, P. A., & Pifer, H, "Prediction of bank failures", The Journal of Finance, 25, 1970, pp.853-868. https://doi.org/10.1111/j.1540-6261.1970.tb00558.x
  5. Dimitras, A. I., Zanakis, S. H., & Zopounidis, C, "A survey of business failure with an emphasis on prediction methods and industrial applications", European Journal of Operational Research, 90(3), 1996 , pp.487-513. https://doi.org/10.1016/0377-2217(95)00070-4
  6. Ohlson, J, "Financial ratios and the probabilistic prediction of bankruptcy", Journal of Accounting Research, 18(1),1980 , pp.109-131. https://doi.org/10.2307/2490395
  7. Pantalone, C., & Platt, M. B, "Predicting commercial bank failure since deregulation", New England Economic Review, 1987, pp.37-47.
  8. Han, I., Chandler, J. S., & Liang, T. P, "The impact of measurement scale and correlation structure on classification performance of inductive learning and statistical methods", Expert System with Applications, 10(2), 1996, pp.209-221. https://doi.org/10.1016/0957-4174(95)00047-X
  9. Shaw, M., & Gentry, J, "Using and expert system with inductive learning to evaluate business loans", Financial Management, 17(3), 1998, pp.45-56.
  10. Buta, P, "Mining for financial knowledge with CBR", AI Expert, 9(10), 1994, pp.34-41.
  11. Bryant, S. M, "A case-based reasoning approach to bankruptcy prediction modeling", International Journal of Intelligent Systems in Accounting, Finance and Management, 6(3), 1997, pp.195-214. https://doi.org/10.1002/(SICI)1099-1174(199709)6:3<195::AID-ISAF132>3.0.CO;2-F
  12. Bortiz, J. E., & Kennedy, D. B, "Effectiveness of neural network types for prediction of business failure", Expert Systems with Application, 9(4), 1995, pp.503-512. https://doi.org/10.1016/0957-4174(95)00020-8
  13. Zhang, G., Hu, M. Y., Patuwo, B. E., & Indro, D. C, "Artificial neural networks in bankruptcy prediction: General framework and crossvalidation analysis", European Journal of Operational Research, 116(1), 1999 , pp.16-32. https://doi.org/10.1016/S0377-2217(98)00051-4
  14. Coakley, J. R., & Brown, C. E, "Artificial neural networks in accounting and finance: Modeling issues", International Journal of Intelligent Systems in Accounting, Finance and Management, 9(2), 2000, pp.119-144. https://doi.org/10.1002/1099-1174(200006)9:2<119::AID-ISAF182>3.0.CO;2-Y
  15. Fan, A., & Palaniswami, M, "Selecting bankruptcy predictors using a support vector machine approach", Proceeding of the international joint conference on neural network, Vol. 6, 2000, pp. 354-359.
  16. Van Gestel, T., Baesens, B., Suykens, J., Espinoza, M. Baestaens, D.-E., Vanthienen, J., et al. "Bankruptcy prediction with least squares support vector machine classifiers, computational intelligence for financial engineering", 2003, proceeding 2003. IEEEE international conference on 2003, pp.1-8.
  17. Min, S.,& Lee, J., "Hybrid genetic algorithms and support vector machines for bankruptcy prediction", Expert Systems with Applications, Volume 31, Issue 3, October 2006, pp.652-660. https://doi.org/10.1016/j.eswa.2005.09.070
  18. 신택수, 홍태호, "AdaBoost 알고리즘 기반 SVM을이용한 부실 확률분포 기반의 기업신용평가", 지능정보연구, 17권 3호(2011), pp.25-41.
  19. 김명종, "유전자 알고리즘을 이용한 분류기 앙상블의 최적 선택", 지능정보연구 제16권 제4호 2010, pp. 99-112.
  20. 김승혁, 김종우, "Modified Bagging Predictors를 이용한 SOHO 부도 예측", 한국지능정보시스템학회논문지 제13권 제2호, 2007, pp.15-26.
  21. Dietterich, T. G, "Machine-learning research: Four current directions", AI Magazine, 18(4), 1997, pp.97-136.
  22. Kuncheva L.I, "Combining classifiers: Soft computing solutions", in: S.K. Pal and A. Pal (Eds.) Pattern Recognition: From Classical to Modern Approaches, World Scientific Publishing Co., Singapore, 2001, 427-452
  23. Breiman, L, "Bagging predictors", Machine Learning, 24(2), 1996, pp.123-140.
  24. Vapnik, V. N, "The nature of statistical learning theory", New York: Springer, 1995.
  25. 박창석, 김병만, 서병훈, 김준우,이광호,"이동 차량에서의 실시간 자동차 번호판 인식",한국산업정보학회논문지, v.9, no.2, 2004년, pp.32-43
  26. 원철호, 이상헌, 이태균,"인터랙티브 TV 컨트롤 시스템을 위한 근적외선 영상의 얼굴 인식", 한국산업정보학회논문지, v.15, no.5, 2010년, pp.11-17
  27. 유혜경, 이진영, 나종화,"매장문화재 예측을 위한 통계적 분류 분석", 한국산업정보학회논문지, v.14, no.3, 2009년, pp.106-113
  28. Ho, T. K, "The random subspace method for constructing decision forests", IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 1998, pp.832-844. https://doi.org/10.1109/34.709601

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