Automated knowledge acquisition for Prospector-like expert systems

  • Petr Berka
  • Jiří Ivánek
Extended Abstracts
Part of the Lecture Notes in Computer Science book series (LNCS, volume 784)

Abstract

The method for automatic knowledge acquisition from categorical data is explained. Empirical implications are generated from data according to their frequencies. Only those of them are inserted to created knowledge base whose validity in data statistically significantly differs from the weight composed by the Prospector like inference mechanism from the weights of the implications already present in the base. A comparison with classical machine learning algorithms is discussed. The method is implemented as a part of the Knowledge EXplorer system.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Berka, P.: Knowledge EXplorer. A Tool for Automated Knowledge Acquisition from Data. TR-93-03, OeFAI Technical Report, Vienna, 1993.Google Scholar
  2. 2.
    Berka, P.: A Comparison of Three Different Methods for Acquiring Knowledge about Virological Hepatitis Tests. TR-93-10, OeFAI Technical Report, Vienna, 1993.Google Scholar
  3. 3.
    Biggs, D., de Ville, B., Suen, E.: A method of choosing multiway partitions for classification and decision trees. Journal of Applied Statistics, Vol. 18, No. 1, 1991, 49–62.Google Scholar
  4. 4.
    Clark, P.: Functional Specification of CN and AQ. TI/P2154/PC/4/1.2, Turing Institute, 1989.Google Scholar
  5. 5.
    Duda, R.O., Gasching, J.E.: Model Design in the Prospector Consultant System for Mineral Exploration. in: Michie, D. (ed.), Expert Systems in the Micro Electronic Age, Edinburgh University Press, UK, 1979.Google Scholar
  6. 6.
    Hájek, P.: Combining Functions for Certainty Factors in Consulting Systems. Int. J. Man-Machine Studies 22, 1985, 59–76.Google Scholar
  7. 7.
    Ivánek, J., Stejskal, B.: Automatic Acquisition of Knowledge Base from Data without Expert: ESOD (Expert System from Observational Data), in: Proc. COMPSTAT'88 Copenhagen (Physica-Verlag Heildelberg 1988), 175–180.Google Scholar
  8. 8.
    Murphy, P.M., Aha, D.W.: UCI Repository of Machine Learning Databases. Irvine, University of California, Dept. of Information and Computer Science.Google Scholar
  9. 9.
    Thrun S.B. et al: The Monk's problems. A Performance Comparision of Different Learning Algorithms, Carnegie Mellon University 1991, 154p.Google Scholar
  10. 10.
    Winkelbauer, L.-Berka, P.: New Algorithms for ALEX: Expanding An Integrated Learning Environment. in: Proc. ECML93 workshop on integrated learning architecture, 1993.Google Scholar

Copyright information

© Springer-Verlag 1994

Authors and Affiliations

  • Petr Berka
    • 1
  • Jiří Ivánek
    • 1
  1. 1.Dept. of Information and Knowledge EngineeringPrague University of EconomicsPrague

Personalised recommendations