Learning problem-oriented decision structures from decision rules: The AQDT-2 system

  • Ryszard S. Michalski
  • Ibrahim F. Imam
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 869)


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.

Key words

machine learning inductive learning decision structures decision rules attribute selection 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    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.Google Scholar
  2. 2.
    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.Google Scholar
  3. 3.
    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.Google Scholar
  4. 4.
    Bohanec, M. and Bratko, I., “Trading Accuracy for Simplicity in Decision Trees”, Machine Learning Journal, Vol. 15, No. 3, Kluwer Academic Publishers, 1994.Google Scholar
  5. 5.
    Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J., “Classification and Regression Structures”, Belmont, California: Wadsworth Int. Group, 1984.Google Scholar
  6. 6.
    Cestnik, B. & Bratko, I., “On Estimating Probabilities in Structure Pruning”, Proceeding of EWSL 91, (pp. 138–150) Porto, Portugal, March 6–8, 1991.Google Scholar
  7. 7.
    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.Google Scholar
  8. 8.
    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.Google Scholar
  9. 9.
    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.Google Scholar
  10. 10.
    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.Google Scholar
  11. 11.
    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.Google Scholar
  12. 12.
    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.Google Scholar
  13. 13.
    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.Google Scholar
  14. 14.
    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.Google Scholar
  15. 15.
    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.Google Scholar
  16. 16.
    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).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Ryszard S. Michalski
    • 1
  • Ibrahim F. Imam
    • 1
  1. 1.Center for Artificial IntelligenceGeorge Mason UniversityFairfax

Personalised recommendations