Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
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Selection of Input Nodes in Artificial Neural Network for Bankruptcy Prediction by Link Weight Analysis Approach

연결강도분석접근법에 의한 부도예측용 인공신경망 모형의 입력노드 선정에 관한 연구

  • Published : 2001.12.01
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Abstract

Link weight analysis approach is suggested as a heuristic for selection of input nodes in artificial neural network for bankruptcy prediction. That is to analyze each input node\\\\`s link weight-absolute value of link weight between an input node and a hidden node in a well-trained neural network model. Prediction accuracy of three methods in this approach, -weak-linked-neurons elimination method, strong-linked-neurons selection method and integrated link weight model-is compared with that of decision tree and multivariate discrimination analysis. In result, the methods suggested in this study show higher accuracy than decision tree and multivariate discrimination analysis. Especially an integrated model has much higher accuracy than any individual models.

본 연구에서는 부도예측용 인공신경망의 입력노드 선정을 위한 휴리스틱으로 연결강도분석접근법을 제안한다. 연결강도분석은 학습이 끝난 인공신경망에서 입력노드와 은닉노드를 연결하는 연결가중치의 절대값 즉, 연결강도를 분석하여 입력변수를 선정하는 접근법으로, 선정기준에 따라 약체연결뉴론제거법과 강체연결뉴론선택법을 들 수 있다. 본 연구에서는 약체연결뉴론제거법, 강체연결뉴론선택법 그리고 이 두 기법을 통합한 통합 연결강도 모형을 제안하여 각각 의사결정트리 및 다변량판별분석에 의해 선정된 입력변수를 이용한 인공신경망 모형과 예측율을 비교한다. 실험 결과 본 연구에서 제안하고 있는 방법론이 의사결정트리나 다변량판별분석 기법보다 높은 예측율을 보여주었다. 특히 두 기법의 통합연결강도 모형의 경우에는 다른 단일 기법보다 높은 예측율을 보이고 있다.

Keywords

References

  1. 경영분석 박정식;신동령;
  2. 2000 추계정치학술대회,한국지능정보시스템학회 연결강도판별분석에 의한 부도예측용 신경망 모형의 입력노드 설계 : 강체 연결뉴론 선정및 약체 연결뉴론 제거 접근법 이웅규;손동우
  3. 2000 추계정치학술대회,한국지능정보시스템학회 연결강도판별분석에 의한 부도예측용 신경망 모형의 입력노드 설계:강체 연결뉴론 선정및 약체 연결뉴론 제거 접근법 이웅규;손동우
  4. 전문가시스템 원리와 개발 이재규;최형림;김현수;서민수;주석진;지원철
  5. 한국 경영과학회지 유전자 알고리즘을 이용한 인공신경망의 구조 설계 이재식;차봉근;
  6. 한국과학기술원(박사학위논문) 도산예측을 위한 인공지능 방법과 통계적 방법의 통합 방법론 조홍규
  7. 사회과학 조사방법론 v.2 채서일;
  8. Journal of Finace,Sep Financial Ratios,Discriminant Analysis and prediction of Corporate Bankruptcy Altman,Edward I.
  9. Journal of Accounting and Finance 18 Accounting Implications of Failure Prediciton Models Altman,Edward I
  10. John Wiley and Sons Data Mining Techniques : for Marketing Sales and Customer Support Berry,Michael J.A.;Gordon Linoff
  11. Prentice Hall,Upper Saddle River,NJ Vasant and Rogar Stein,Intelligent Decision Support Methods;The Science of Knowledge Work Dahr
  12. Iformation and Management v.24 no.3 Forecasting with Neural Networks : an Application using Bankruptcy Data Fletcher,D;E,Goss
  13. Addison Wesely Introduction to the Theory of Nueral Computation Hertz,John,Anders Krogh and Richard G.Palmer
  14. In Mozer,M.C.,M.I.Jordan and T.Petsche(eds)Advances in Neural Information Processing System9 Balancing between Bagging and Bumpling Heskes,T.
  15. Decision Support Systems v.18 Hybrid Neural Network Models for Bankruptcy Predictions Lee,Kun Chang,Ingoo Han;YoungsigKwon
  16. Management Science v.34 no.12 Inducing Rules for Expert System Development : An Example Using Default and Bankruptcy Data Messier,William F.Jr.;James V.Hansen
  17. In Proceedings of the IEEE International Conference on Neural Network A Neural network Model for Bankruptcy Prediction Odom,M.;R.Sharda
  18. Journal of Accounting Research v.18 no.1 Financial Ratios and The Probabilistic Prediction of Bankruptcy Ohlson,J.
  19. In Mozer,M.C.,M.I.Jordan,and T.Petsche(eds) Advances in Neural Information Processing Systems 9 Generating Accurate and Diverse Members of Neural-Network Ensemble Opitz D.W.,;J.W.Shavlik
  20. Programs for Machine Learning Morgan Kauffman,Mountain View,CA Quinlan,J.R.C4.5
  21. In Proceedings of the 24th Hawaii International Conference on System Science A Neural Network Approach to Bankruptcy Prediction Raghupathi,W.L.L. Schkade,;B.S.Raju
  22. Decision Sciences v.24 no.4 Neural Networks:A New Tool for Predicting Thrift Failures Salchengerger,L.M.E.M,Cinar;N.A.Lash
  23. Decision Support Systems v.17 Self Organizing Neural Networks for Financial Diagnosis Serrano-Cinca, Carlos
  24. Proceedings of the 5th Asia-Pacific Decision Sciences Institute Conference A GA-based Input Selection Approach for Neural Networks Modeling : Application to Bankruptcy Prediction Shin,Kyung-shik;Seung-hyun Hong
  25. Journal of Management Information Systems v.16 no.1 Dynamics of Modeling in Data Mining : Interpretive Approach to Bankruptcy Prediction Sung,Tae Kyoung.,Namsik Chang ;Gunhee Lee
  26. Management Science v.38 no.7 Managerial Applications of Neural Networks : The Case of Bank Failure Predictions Tam,Kar Yan And Melody Y Kiang
  27. Computers and Industrial Engineering v.27(1-4) Predicting Japanese Corporate Bankruptcy in terms of Financial Data Using Neural Network Tsukuda,J.;S.L,Bada
  28. Computers and Industrial Engineering v.25 Neural Network Performance on the Bankruptcy Classification Problem Udo,G.,
  29. Decision Support Systems v.11 Bankruptcy Prediction using Neural Networks Wilson,Rick L.;Ramesh Sharda
  30. European Journal of Operational Research v.116 Artificial Neural Networks in Bankruptcy Prediction : General Framework and Cross Validation Analyiss Zhang,Guoqiang,Michael Y.Hu,B.Eddy Patuwo,and Daniel C Indro
  31. Journal of Accounting Research v.22 Methodological Issues Related to the Estimation of Financial Distress Prediction Models Zmijewski,Mafke