Neural networks, decision tree induction and discriminant analysis: An empirical comparison

Journal of the Operational Research Society

Volumn 45, Issue 4, 1994, Pages 440-450

  Curram, Stephen P ^a   Mingers, John ^a  

^a UNIVERSITY OF WARWICK   (United Kingdom)

Author keywords

Classification; Decision trees; Discriminant analysis; Induction; Neural networks

Indexed keywords

ADMINISTRATIVE DATA PROCESSING; CLASSIFICATION (OF INFORMATION); DATA STRUCTURES; DECISION THEORY; MATHEMATICAL MODELS; NEURAL NETWORKS; PERFORMANCE; PROGRAM PROCESSORS; STATISTICAL METHODS; TREES (MATHEMATICS);

DATA SETS; DECISION TREE INDUCTION; DISCRIMINANT ANALYSIS; REPLICATION; VARIANCE;

OPERATIONS RESEARCH;

EID: 0028416730     PISSN: 01605682     EISSN: 14769360     Source Type: Journal    
DOI: 10.1057/jors.1994.62     Document Type: Article

References (28)

1. A. Hart (1992) Using neural networks for classification tasks—some experiments on datasets, and practical advice. J. Opl Res. Soc. 43, 215-226.
2. Y. Yoon, G. Swales Jr and T. M. Margavio (1993) A comparison of discriminant analysis versus artificial neural networks. J. Opl Res. Soc. 44, 51-60.
3. R. P. Lippman (1987) An introduction to computing with neural nets. IEEE ASSP Magazine 4(2), 4-22.
4. P. D. Hawley, J. D. Johnson and D. Raina (1990) Artificial neural systems: a new tool for financial decision making. Financial Analysts J. 46(6), 63-72.
5. L. I. Burke (1991) Introduction to artificial neural systems for pattern recognition. Comp Ops Res. 18, 211-220.
6. D. E. Rumelhart, G. E. Hinton and R. J. Williams (1986) Learning internal representations by error propagation. In Parallel Distributed Processing, Vol 1. (D. E. Rumelhart, J. L. McClelland and the PDP Research Group, Eds) pp. 318-362. MIT Press, Cambridge, MA.
7. T. P. Vogl, J. K. Mangis, A. K. Rigler, W. T. Zink and D. L. Alkon (1988) Accelerating the convergence of the back-propogation method. Biol. Cyb. 59, 257-263.
8. A. M. Wildberger (1990) Neural networks as a modelling tool. In Proceedings of the SCS Eastern Multiconference, (W. Webster and J. Uttamsingh, Eds) April 1990, Nashville Tennessee, pp. 65-74. Society for Computer Simulation. San Diego, California.
9. J. Sietsma and R. J. F. Dow (1991) Creating artificial neural networks that generalize. Neural Networks 4, 67-79.
10. S. C. Huang and Y. F. Huang (1991) Bounds on the number of hidden neurons in multilayer perceptrons. IEEE Trans. Neural Networks 2(1), 47-55.
11. R. G. Hoptroff (1993) The principles and practice of time series forecasting and business modelling using neural networks. Neural Comput. Applic. 1(1), 59-66.
12. R. A. Fisher (1936) The use of multiple measurements in taxonomic problems. Ann. Eugenics 7, 179-188.
13. R. Michalski, J. Carbonell and T. Mitchell (1983) Machine Learning: An Artificial Intelligence Approach Vol 1 Morgan Kaufman, Los Altos.
14. R. Michalski, J. Carbonell and T. Mitchell (1986) Machine Learning: An Artificial Intelligence Approach Vol 1 Morgan Kaufman, Los Altos.
15. I. Bratko and N. Lavrac (Eds) (1987) Progress in Machine Learning. Sigma Press, UK.
16. E. Hunt, J. Marin and P. Stone (1966) Experiments in Induction. Academic Press, New York.
17. J. R. Quinlan (1983) Learning efficient classification procedures and their application to chess end games. In Machine Learning: An Artificial Intelligence Approach, Vol. 1. (R. Michalski, J. Carbonell and T. Mitchell, Eds) pp. 463-482. Morgan Kaufman, Los Altos.
18. L. Breiman, J. Freidman, R. Olshen and C. Stone (1984) Classification and Regression Trees. Wadsworth International, California.
19. J. R. Quinlan (1987) Simplifying decision trees. Int. J. Man-Machine Studies 27, 221-234.
20. T. Niblett (1987) Constructing decision trees in noisy domains. In Progress in Machine Learning (I. Bratko and N. Lavrac, Eds) pp. 67-79. Sigma Press, UK.
21. J. Mingers (1987). Expert systems—rule induction with statistical data. J. Opl Res. Soc. 38, 39-47.
22. J. Mingers (1987) Rule induction with statistical data—a comparison with multiple regression. J. Opl Res. Soc. 38, 347-352.
23. J. Mingers (1989) An empirical comparison of selection measures for decision-tree induction. Machine Learning 3, 319-342.
24. J. Mingers (1989) An empirical comparison of pruning methods for decision-tree induction. Machine Learning 4, 227-243.
25. I. Bratko and I. Konenko (1986) Learning diagnostic rules from incomplete and noisy data. Seminar on Al methods in statistics. London Business School, UK, Unicom Seminars Ltd.
26. M. Kendall and A. Stewart (1976) The Advanced Theory of Statistics (Vol 3). Griffen, London.
27. W. Cochran (1947) Some consequencies when the assumptions for the Analysis of Variance are not satisfied. Biometrica 3, 22-38.
28. SAS Institute Inc (1988) SAS/STAT User's Guide, Release 6.03 Edition. Cary, NC: SAS Institute Inc.