Abstract
This paper concerns the issue of what is the best form for learning, representing and using knowledge for decision making. The proposed answer is that such knowledge should be learned and represented in a declarative form. When needed for decision making, it should be efficiently transferred to a procedural form that is tailored to the specific decision making situation. Such an approach combines advantages of the declarative representation, which facilitates learning and incremental knowledge modification, and the procedural representation, which facilitates the use of knowledge for decision making. This approach also allows one to determine decision structures that may avoid attributes that unavailable or difficult to measure in any given situation. Experimental investigations of the system, FRD-1, have demonstrated that decision structures obtained via the declarative route often have not only higher predictive accuracy but are also are simpler than those learned directly from facts.
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References
Arciszewski, T, Bloedorn, E., Michalski, R., Mustafa, M., and Wnek, J., “Constructive Induction in Structural Design”, Reports of Machine Learning and Inference Laboratory, MLI-92-7, George Mason University, 1992.
Bloedorn, E., Wnek, J., Michalski, R.S., and Kaufman, K., “AQ17: A Multistrategy Learning System: The Method and User's Guide”, Reports of Machine Learning and Inference Laboratory, MLI-93-12, George Mason University, 1993.
Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J., “Classification and Regression Structures”, Belmont, California: Wadsworth Int. Group, 1984.
Clark, P. & Niblett, T., “Induction in Noisy Domains” in I. Bratko and N. Lavrac, (Eds.), Progress in Machine Learning, Sigma Press, Wilmslow, 1987.
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, 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.
Kohavi, R., “Bottom-Up Induction of Oblivious Read-Once Decision-Graphs: Strengths and Limitations”, Proceedings of AAAI-94, pp. 613–18, Seattle, 1994.
Michie, D., Muggleton, S., Page, D. and Srinivasan, A., “International East-West Challenge”, Oxford University, UK, 1994.
Michalski, R.S, “Designing Extended Entry Decision Tables and Optimal Decision Trees Using Decision Diagrams”, Technical Report No.898, Urbana: University of Illinois, 1978.
Michalski, R.S., Mozetic, I., Hong, J. and 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.
Michalski, R.S., and Imam, I.F., “Learning Problem-oriented Decision Structures from Decision Rules: The AQDT-2 System”, in The Proceedings of the International Symposium on Methodology for Intelligent Systems, ISMIS-94, Charlotte, NC, October, 1994.
Mingers, J., “An Empirical Comparison of selection Measures for Decision-Structure Induction”, Machine Learning, Vol. 3, No. 3, (pp. 319–342), Kluwer Academic Publishers, 1989.
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.
Thrun, S.B., Mitchell, T. and Cheng, J., (Eds.) “The MONK's Problems: A Performance Comparison of Different Learning Algorithms”, Technical Report, Carnegie Mellon University, October, 1991.
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© 1996 Springer-Verlag Berlin Heidelberg
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Imam, I.F., Michalski, R.S. (1996). Learning for decision making: The FRD approach and a comparative study. In: Raś, Z.W., Michalewicz, M. (eds) Foundations of Intelligent Systems. ISMIS 1996. Lecture Notes in Computer Science, vol 1079. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61286-6_167
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DOI: https://doi.org/10.1007/3-540-61286-6_167
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