Abstract
A decision structure is an acyclic graph that specifies an order of tests to be applied to an object (or a situation) to arrive at a decision about that object. and serves as a simple and powerful tool for organizing a decision process. This paper proposes a methodology for learning decision structures that are oriented toward specific decision making situations. The methodology consists of two phases: 1—determining and storing declarative rules describing the decision process, 2—deriving on-line a decision structure from the rules. The first step is performed by an expert or by an AQ-based inductive learning program that learns decision rules from examples of decisions (AQ15 or AQ17). The second step transforms the decision rules to a decision structure that is most suitable for the given decision making situation. The system, AQDT-2, implementing the second step, has been applied to a problem in construction engineering. In the experiments, AQDT-2 outperformed all other programs applied to the same problem in terms of the accuracy and the simplicity of the generated decision structures.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Arciszewski, T., Bloedorn, E., Michalski, R., Mustafa, M., and Wnek, J., “Constructive Induction in Structural Design”, Report of Machine Learning and Inference Laboratory, MLI-92-7, Center for AI, George Mason University., 1992.
Bergadano, F., Matwin, S., Michalski R. S. and Zhang, J., “Learning Two-tiered Descriptions of Flexible Concepts: The POSEIDON System,” Machine Learning, Vol. 8, No. 1, pp. 5–43, January 1992.
Bloedorn, E., Wnek, J., Michalski, R.S., and Kaufman, K., “AQ17: A Multistrategy Learning System: The Method and User's Guide”, Report of Machine Learning and Inference Laboratory, MLI-93-12, Center for AI, George Mason University. 1993.
Bohanec, M. and Bratko, I., “Trading Accuracy for Simplicity in Decision Trees”, Machine Learning Journal, Vol. 15, No. 3, Kluwer Academic Publishers, 1994.
Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J., “Classification and Regression Structures”, Belmont, California: Wadsworth Int. Group, 1984.
Cestnik, B. & Bratko, I., “On Estimating Probabilities in Structure Pruning”, Proceeding of EWSL 91, (pp. 138–150) Porto, Portugal, March 6–8, 1991.
Cestnik, B. & Karalic, A., “The Estimation of Probabilities in Attribute Selection Measures for Decision Structure Induction” in Proceeding of the European Summer School on Machine Learning, July 22–31, Priory Corsendonk, Belgium, 1991.
Imam, I.F. and Michalski, R.S., “Learning Decision Structures from Decision Rules: A method and initial results from a comparative study”, in Journal of Intelligent Information Systems JIIS, Vol. 2, No. 3, pp. 279–304, Kerschberg, L., Ras, Z., & Zemankova, M. (Eds.), Kluwer Academic Pub., MA, 1993.
Imam, I.F., Michalski, R.S. and Kerschberg, L., “Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques”, Proceeding of the First International Workshop on Knowledge Discovery in Database, Washington, D.C., July, 11–12, 1993.
Michalski, R.S., “AQVAL/1-Computer Implementation of a Variable-Valued Logic System VL1 and Examples of its Application to Pattern Recognition”, Proceeding of the First International Joint Conference on Pattern Recognition, (pp. 3–17), Washington, DC, October 30–November 1, 1973.
Michalski, R.S., Mozetic, I., Hong, J. & Lavrac, N., “The Multi-Purpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains”, Proceedings of AAAI-86, (pp. 1041–1045), Philadelphia, PA, 1986.
Mingers, J., “An Empirical Comparison of selection Measures for Decision-Structure Induction”, Machine Learning, Vol. 3, No. 3, (pp. 319–342), Kluwer Academic Publishers, 1989a.
Quinlan, J.R., “Discovering Rules By Induction from Large Collections of Examples”, in D. Michie (Edr), Expert Systems in the Microelectronic Age, Edinburgh University Press, 1979.
Quinlan, J.R., “Learning efficient classification procedures and their application to chess end games” in R.S. Michalski, J.G. Carbonell and T.M. Mitchell, (Eds.), Machine Learning: An Artificial Intelligence Approach. Los Altos: Morgan Kaufmann, 1983.
Quinlan, J. R. “Probabilistic decision structures,” in Y. Kodratoff and R.S. Michalski (Eds.), Machine Learning: An Artificial Intelligence Approach, Vol. III, San Mateo, CA, Morgan Kaufmann Publishers, (pp. 63–111), June, 1990.
Wnek, J., “A Fast Re-Implementation of the AQ-based Inductive Learning Program for Large Datasets: AQ15c,” Reports of Machine Learning and Inference Laboratory, MLI 94-3, Center for Artificial Intelligence, George Mason University, 1994 (to appear).
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1994 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Michalski, R.S., Imam, I.F. (1994). Learning problem-oriented decision structures from decision rules: The AQDT-2 system. In: Raś, Z.W., Zemankova, M. (eds) Methodologies for Intelligent Systems. ISMIS 1994. Lecture Notes in Computer Science, vol 869. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58495-1_42
Download citation
DOI: https://doi.org/10.1007/3-540-58495-1_42
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-58495-7
Online ISBN: 978-3-540-49010-4
eBook Packages: Springer Book Archive