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The Effect of Training Data Set Size and the Complexity of the Separation Function on Neural Network Classification Capability: The Two-Group Case

  • Moshe Leshno
  • Yishay Spector

Abstract

Classification among groups is a crucial problem in managerial decision making. Classification techniques are used in: identifying stressed firms, classifying among consumer types, rating of firms’ bonds, etc. Neural networks are recognized as important and emerging methodologies in the area of classification. In this paper, we study the effect of training sample size and the neural network topology on the classification capability of neural networks. We also compare neural network capabilities with those of commonly used statistical methodologies. Experiments were designed and carried out on two-group classification problems to find answers to these questions.

Keywords

Hide Layer Linear Discriminant Analysis Discriminant Function Neural Network Model Hide Unit 
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References

  1. Altman, E.L., “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy,” Journal of Finance 23, 3 (1968):589–609.CrossRefGoogle Scholar
  2. Altman, E.L., R.G. Haldeman, and P. Narayanan, “Zeta Analysis,” Journal of Banking and Finance (1977):29–51.Google Scholar
  3. Altman, E.L., “Accounting Implications of Failure Prediction Models,” Journal of Accounting Auditing & Finance (1982):4–19.Google Scholar
  4. Anderson and Bahadur, “Classification Into Two Multivariate Normal Distributions With Different Covariance Matrices,” The Annals of Mathematical Statistics, 33 (1962):420–431.MathSciNetCrossRefGoogle Scholar
  5. Awh, R.Y., and D.A. Waters, “A Discriminant Analysis of Economic, Demographic and Attitude Characteristics of Bank Charge-Card Holders: A Case Study,” Journal of Finance 29 (1974):973–980.CrossRefGoogle Scholar
  6. Baldwin, J., and G.W. Glezen, “Bankruptcy Prediction Using Quarterly Financial Statement Data,” Journal of Accounting Auditing & Finance (1989):24–29.Google Scholar
  7. Beaver, W., “Financial Ratios and Predictors of Failure,” Empirical Research in Accounting: Selected Studies (1966):71–111.Google Scholar
  8. Breiman, L., J.H. Friedman, R.A. Olshen, and C.J. Stone, Classification and Regression Trees, Belmont, CA: Wadsworth (1984).MATHGoogle Scholar
  9. Cadden, D.T., “Neural Networks and the Mathematics of Chaos-An Investigation of These Methodologies as Accurate Predictors of Corporate Bankruptcy” (1982).Google Scholar
  10. Collins, R.A. and R.D. Green, “Statistical Methods of Bankruptcy Forecasting,” Journal of Economics and Busines 32 (1982):349–354.CrossRefGoogle Scholar
  11. Leshno, M., Ya.V. Lin, A. Pinkus, and S. Schocken, “Multilayer Feedforward Networks with a Non-Polynomial Activation Function Can Approximate Any Function,” Neural Networks 6 (1993):861–867.CrossRefGoogle Scholar
  12. Odom, M., and R. Sharda, “A Neural Network Model for Bankruptcy Prediction,” Proceedings of the IEEE International Conference on Neural Networks, San Diego, CA: (1990):II163–II168.Google Scholar
  13. Press, S.J., and S. Wilson, “Choosing between Logistic Regression and Discriminant Analysis,” Journal of The American Statistical Association 73 (1978):699–705.MATHCrossRefGoogle Scholar
  14. Raghupathi, W., L.L. Schkade, and B.S. Raju “A Neural Network Approach to Bankruptcy Prediction,” Proceedings of the IEEE 24 th Annual Hawaii International Conference on Systems Science (1991).Google Scholar
  15. Tam, K.Y. and M.Y. Kiang, “Managerial Application of Neural Networks: The Case of Bank Failure Prediction,” Management Science 38, 7 (1990):926–947.CrossRefGoogle Scholar
  16. Trippi, R.R., and E. Turban (eds.), Neural Networks in Finance and Investing, Chicago: Probus (1992).Google Scholar
  17. Vapnik, V.N. and Ya.A. Chervonenkis, “On the Uniform Convergence of Relative Frequencies Of Events To Their Probabilities,” Theoretical. Probability and Its Application 16, 2 (1971):264–280.MathSciNetMATHCrossRefGoogle Scholar
  18. Vapnik, V.N., Estimation of Dependence Based on Empirical Data, New York: Springer-Verlag (1982).Google Scholar
  19. Vapnik, V.N., Measuring the Capacity of Learning Machine, N.J.: AT&T Bell Laboratory, (1992).Google Scholar
  20. Welch, B.A. “Note on Discriminant Functions,” Biometrika 31 (1939):218–220.MathSciNetMATHGoogle Scholar
  21. West, R.G., “A Factor-Analytic Approach to Bank Condition,” Journal of Banking and Finance, 9 2 (1985):253–266.CrossRefGoogle Scholar

Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Moshe Leshno
    • 1
  • Yishay Spector
    • 1
  1. 1.School of Business AdministrationThe Hebrew University of JerusalemMount Scopus, JerusalemIsrael

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