Stat: A Probabilistic Knowledge Based Induction Program for Building Expert Systems

  • Rense Lange
Conference paper


This report describes the STAT computer program designed to induce classification rules from example cases. The purpose of this system is to automate the knowledge acquisition process for the construction of new expert systems. Two major features of STAT are its capability to induce classification trees from noisy data and the possibility to include domain relevant knowledge to guide the induction process in accordance with experts’ conceptualization of the domain.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Arbib, B. and D. Michie (1985). Generating Rules From Examples. IJCAI9, 631–633.Google Scholar
  2. Dietterich, T.G. and R.S. Michalski (1983). A Comparative Review of Selected Methods for Learning From Examples. In: Michalski Scholar
  3. Hunt, E.B., J.B. Marin, and P.T. Stone (1966). Experiments in Induction, Academic Press, New York.Google Scholar
  4. Lange, R. and M.T. Harandi (1985). Human Engineering Aspects of a Program Debugging Expert System. Proceedings IEEE COMPSAC ’85, (in press).Google Scholar
  5. Michalski, R.S. (1983) A Theory and Methodology of Inductive Learning. In: Michalski Scholar
  6. Michalski, R.S., J.G. Carbonell, and T.M. Mitchell (Eds.) (1983). Machine Learning, Tioga, Palo Alto.Google Scholar
  7. Michalski, R.S. and R.L. Chilausky (1980). Learning by Being Told and Learning From Examples: An Experimental Comparison of Two Methods of Knowledge Acquisition in the Context of Developing an Expert System for Soybean Disease Diagnosis, Policy Analysis and Information Systems, Vol. 4, No. 2.Google Scholar
  8. Mueller, J., K. Schuessler, and H. Costner (1970). Statistical Reasoning in Sociology, Houghton Mifflin, Boston.Google Scholar
  9. Nilsson, N.J. (1980). Principles of Artificial Intelligence, Tioga, Palo Alto.zbMATHGoogle Scholar
  10. Quinlan, J.R. (1979). Discovering Rules From Large Collections of Examples: a Case Study. In: D. Michie (Ed.). Expert Systems in the Micro Electronic Age, Edinburgh University Press.Google Scholar
  11. Quinlan, J.R. (1983). Learning Efficient Classification Procedures and Their Application to Chess Endgames. In: Michalski Scholar
  12. Shapiro, A. and T. Niblett (1982). Automatic Induction of Classification Rules for Chess Endgames. In: M.R.B. Clarke (Ed.). Advances in Computer Chess 3. Edinburgh University Press.Google Scholar
  13. Siegel, S. (1956). Non-Parametric Statistics, Wiley, New-York.Google Scholar
  14. Taylor, J. (1984). Putting a Ph.D. in Your PC. PC Magazine, February, 167–174.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1986

Authors and Affiliations

  • Rense Lange
    • 1
  1. 1.Dept. of Computer ScienceUniversity of Illinois at Urbana-Champaign1304 W. Springfield AvenueUrbana, I1USA

Personalised recommendations