Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Prediction of bankruptcy data using machine learning techniques

기계학습 방법을 이용한 기업부도의 예측

  • Park, Dong-Joon (Department of Statistics, Pukyong National University) ;
  • Yun, Ye-Boon (Faculty of Environmental and Urban Engineering, Kansai University) ;
  • Yoon, Min (Department of Statistics, Pukyong National University)
  • 박동준 (부경대학교 통계학과) ;
  • 윤예분 (간사이대학교 수리계획공학연구실) ;
  • 윤민 (부경대학교 통계학과)
  • Received : 2012.04.30
  • Accepted : 2012.05.25
  • Published : 2012.05.31
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Abstract

The analysis and management of business failure has been recognized to be important in the area of financial management in the evaluation of firms' performance and the assessment of their viability. To this end, effective failure-prediction models are needed. This paper describes a new approach to prediction of business failure using the total margin algorithm which is a kind of support vector machine. It will be shown that the proposed method can evaluate the risk of failure better than existing methods through some real data.

기업도산에 대한 분석과 관리는 기업의 성과와 성장능력을 평가하는 재무관리 분야에서 중요하게 인식되어 왔다. 결국, 기업도산 예측에 대한 효과적인 모형이 필요하게 된다. 본 논문은 서포트 벡터 기계의 한 종류인 토탈 여유도 알고리즘을 이용하여 기업도산 예측을 위하여 새로운 접근 방법을 서술한다. 몇 개의 실제 자료를 통하여 제안한 방법들이 도산 위험의 평가에서 기존의 방법들보다 개선됨을 확인할 수 있었다.

Keywords

References

  1. 김철응, 윤민 (2003). 서포트 벡터 기계에서 잡음 영향의 효과적 조절. <응용통계연구>, 16, 261-271. https://doi.org/10.5351/KJAS.2003.16.2.261
  2. 박혜정 (2011). 온라인 서포트벡터기계를 이용한 온라인 비정상 사건탐지. <한국데이터정보과학회지>, 22, 207-215.
  3. 석경하 (2010). 최소제곱 서포트벡터기계 형태의 준지도 분류. <한국데이터정보과학회지>, 21, 461-470.
  4. 임주열, 백장선, 김민수 (2010). 서포트벡터머신과 정칙화판별함수를 이용한 비디오 문자인식의 분류성능개선. <한국데이터정보과학회지>, 21, 689-697.
  5. 황창하, 신사임 (2010). 커널기계 기법을 이용한 일반화 이분산자기회귀모형 추정. <한국데이터정보과학회지>, 21, 419-425.
  6. Bartlett, P. and Shawe-Talyor, J. (1999). Generalization performance of support vector machines on other pattern classifiers. In Advances in Kernel Methods-Support Vector learning, edited by Scholkopf, B., Burges, C. J. C., and Smola, A., The MIT Press, London, 43-54.
  7. Benenett, K. P. and Mangasarian, O. L. (1992). Robust linear programming discrimination of two linearly inseparable sets. Optimization Methods and Software, 1, 23-24. https://doi.org/10.1080/10556789208805504
  8. Cherkassky, V. and Mulier, F. (1998). Learning from data concepts, theory, and methods, John Wiley & Sons, New York.
  9. Cristianini, N. and Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernelbased learning methods, Cambridge University Press, Cambridge.
  10. Cortes, C. and Vapnik, V. (1995). Support vector networks. Machine Learning, 20, 273-297.
  11. Eisenbis, R. A. (1978). Problem in applying discriminant analysis in credit scoring models. Journal of Banking and Finance, 2, 205-219. https://doi.org/10.1016/0378-4266(78)90012-2
  12. Glover, F. (1990). Improved linear programming models for discriminant analysis. Decision Sciences, 21, 771-785. https://doi.org/10.1111/j.1540-5915.1990.tb01249.x
  13. Haykin, S. (1998). Neural networks a comprehensive foundation, 2nd ed., Prentice Hall, New Jersey.
  14. Mangasarian, O. L. (1968). Multi surface method of pattern separation. IEEE Transaction on Information Theory, 14, 801-807. https://doi.org/10.1109/TIT.1968.1054229
  15. Nakayama, H., Yun, Y. B. and Yoon, M. (2009) Sequential approximate multiobjective optimization using computational intelligence, Springer, Heidelberg.
  16. Scholkopf, B. and Smola, A. J. (2002). Learning with kernels: Support vector machines, regularization, optimization, and beyond, The MIT Press, London.
  17. Shawe-Taylor, J., Bartlett, P. L., Williamson, R. C. and Anthont, M. (1998). Structural risk minimization over data-dependent hierarchies. IEEE Transaction on Information Theory, 44, 1926-1940. https://doi.org/10.1109/18.705570
  18. Smola, A. J. and Scholkopf, B. (1998). A tutorial on support vector regression, NeuroCOLT2 Technical Report, NeuroCOLT, London.
  19. Yoon, M., Yun, Y. B. and Nakayama, H. (2004). Total margin algorithms in support vector machines. IEICE Transactions on Information and Systems, 87, 1223-1230.
  20. Vapnik, V. N. (1998). Statistical learning theory, John Wiley & Sons, New York.

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