Abstract
This paper describes the inductive learning methods for generating decision rules in decision support systems. Three similarity-based learning systems are studied based on: (1) the AQ-Star method, (2) the Tree-Induction method, and (3) the Probabilistic Learning method. Loan evaluation examples and empirical data are used as a basis for comparing these inductive learning methods on their algorithmic characteristics and decision support performance.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ash, R., 1965, Information Theory, John Wiley & Sons, New York.
Bonczek, R. H., Holsapple, C. W., and Whinston, A. B., 1981, ‘Representing Modeling Knowledge with First Order Predicate Calculus’, Operations Research.
Breiman, L., Freidman, J. H., Olshen, R. A., and Stone, C. J., 1984, Classification and Regression Trees, California: Wadsworth.
Carbonell, 1983, ‘Learning by Analogy: Formulating and Generalizing Plans from Past Experiences’, in Machine Learning, ed. R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, Tioga, Palo Alto, CA.
Davis, R., 1979, ‘Interactive Transfer of Expertise: Acquisition of New Inference Rules’, Artificial Intelligence 12, pp. 121–57.
DeJong, G., 1986, ‘An Approach to Learning from Observation’, in Machine Learning (Vol. II), Michalski et al. (Eds.), Morgan Kaufmann, Los Altos, CA.
Gentry, J., Newbold, P., and Whitford, D., 1985, ‘Classifying Bankrupt Firms with Funds Flow Components’, Journal of Accounting Research 23 (1), pp. 146–160.
Gentry, J., Newbold, P., and Whitford, D., 1988, ‘Predicting Industrial Bond Ratings with a Probit Model and Funds Flow Components’, Financial Review.
Haussler, D., 1987, ‘Bias, Version Spaces, and Valiant's Learning Framework’, Proceedings of the Fourth International Workshop on Machine Learning, pp. 324–336, Irvine, CA.
Henderson, J., 1987, ‘Finding Synergy Between Decision Support Systems and Expert Systems Research’, Decision Sciences 18, pp. 333–349.
Holland, J., 1986, ‘Escaping Brittleness: The Possibility of General-Purpose Learning Algorithms Applied to Parallel Rule-Based Systems’, Machine Learning: An Al Approach, Michalski, Carbonell, and mitchell (Eds.), Tioga Pub. Co., Palo Alto, CA.
Hopfield, J. J., 1982, ‘Neural Networks and Physical Systems with Emergent Collective Computational properties’, Proceedings of the National Academy of Science, April, vol. 79, pp. 2554–2558.
Hunt, E. B., 1962, Concept Learning: An Information Processing Problem, Wiley Co., NY.
Lavrac, N., Mosetic, I., and Kononeko, I., 1986, ‘An Experimental Comparison of Two Learning Programs in Three medical Domains’, Turing Institute Report.
Lee, W. D. and Ray, S., ‘Rule Refinement Using the Probabilistic Rule Generator’, Proceedings of the Fifth National Conference on Artificial Intelligence, pp. 442–447.
Lenat, D. B., 1983, ‘EURISKO: A Program that Learns New Heuristics and Domain Concepts; The Nature of Heuristics III: Program Design and Results’, Artificial Intelligence 21, pp. 61–98.
Messier, W. and Hansen, J. V., 1988, ‘Inducing Rules for Expert System Development: An Example Using Default and Bankruptcy Data’, working paper, Brigham Young University, Provo, UT.
Michalski, R. S., 1983, ‘A Theory and Methodology of Inductive Learning’, in Michalski, R., Carbonell, J., and Mitchell, T. (eds.), Machine Learning, Tioga Publishing Co., Palo Alto, CA.
Mitchell, T., Keller, R., and Kedar-Cabelli, S., 1986, ‘Explanation-based Generalization: A Unifying View’, Machine Learning 1, pp. 47–80.
O'Rorke, P., 1982, ‘A Comparative Study of Inductive Learning Systems AQ11P and ID-3 Using a Chess Endgame Test problem’, Report No. UIUCDCS-F-82-899, Department of Computer Science, University of Illinois at Urbana-Champaign.
Quinlan, J. R., 1979, ‘Discovering Rules from Large Collection of Examples: A Case Study’, in Michie, D. (ed.) Expert Systems in the Micro Electronic Age, University Press: Edinburgh.
Quinlan, J. R., 1986, ‘The Effects of Noise on Concept Learning’, in Machine Learning: An Artificial Intelligence Approach, Michalski et al. (eds.), Tioga, Los Altos, pp. 149–165.
Quinlan, J. R., 1986, ‘Induction of Decision Trees’, Machine Learning 1, pp. 81–106.
Rendell, L., 1983, ‘A New Basis for State-Space Learning Systems and a Successful Implementation’, Artificial Intelligence 20, pp. 369–92.
Rendell, L., 1986, ‘A General Framework for Induction and a Study of Selective Induction’, Machine Learning 1 (2), pp. 177–226.
Rendell, L., Benedict, P., Cho, H., and Seshu, R., 1987, ‘Improving the Design of Rule Learning Systems’, Report No. UIUCDCS-R-87-1395, Department of Computer Science, University of Illinois at Urbana-Champaign.
Rivest, R., 1987, ‘Learning Decision Lists’, Machine Learning 2, pp. 229–246.
Rosenblatt, F., 1962, Principles of Neurodynamics, Spartan.
Rumelhart, D. E. and D. Zipser, 1985, ‘Feature Discovery by Competitive Learning’, Cognitive Science 9, pp. 75–112.
Samuel, A. L., 1967, ‘Some Studies in Machine Learning Using the Game of Checkers II — Recent Progress’', IBM J. of Research and Development 11 (6), pp. 609–617.
Sejnowski, T. J. and Rosenberg, C. R., 1986, ‘NETtalk: A Parallel Network that Learns to Read Aloud’, Johns Hopkins University Electrical Engineering and Computer Science Technical Report, JHU/EECS86/01.
Shannon, C. E., 1948, ‘A Mathematical Theory of Communication’, Bell Syst. Tech, J. 27, pp. 379–423 (July), 623-656 (Oct.).
Shannon, C. E., 1951, ‘Prediction and Entropy of Printed English’, Bell Syst. Tech. J. 30, pp. 50–65 (Jan.).
Shaw, M., 1987, ‘Applying Inductive Learning to Enhance Knowledge-Based Expert Systems’, Decision Support Systems 3 (4), pp. 319–332.
Shaw, M. and Gentry, J., 1989, ‘applying Inductive Learning to Assessing Firms' Financial Health’, in Expert Systems in Economics, Banking, and Management, L. F. Pau et al. (eds.), North Holland, Amsterdam.
Shaw, M. and Gentry, J., 1990, ‘Integrating Inductive Learning and Problem Solving in a Risk Classification Expert System’, IEEE Experts, February (to appear).
Shaw, M. and Gentry, J., 1988, ‘Using an Expert System with Inductive Learning for Evaluating Business Loans’, Financial Management 17 (3), pp. 45–56.
Stepp, R., 1982, ‘A Description and User's Guide for CLUSTER/PAF — A Program for Conjunctive Conceptual Clustering’, Dept. of Computer Science, University of Illinois, Urbana, Rep. UIUCDCS-R82-1084.
Wilkins, D. C., 1988, ‘Apprenticeship Learning Techniques for Knowledge Based Systems’, Report No. STAN-CS-88-1242, Dept. of Computer Science, Stanford University.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Shaw, M.J., Gentry, J.A. & Piramuthu, S. Inductive Learning Methods for Knowledge-Based Decision Support: A Comparative Analysis. Computer Science in Economics and Management 3, 147–165 (1990). https://doi.org/10.1007/BF00436712
Received:
Issue Date:
DOI: https://doi.org/10.1007/BF00436712