Knowledge Discovery with qualitative influences and synergies

  • Jesús Cerquides
  • Ramon López de Màntaras
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1510)


We review some approaches to qualitative uncertainty and propose a new one based on the idea of Absolute Order of Magnitude. We show that our ideas can be useful for Knowledge Discovery by introducing a derivation of the Naive-Bayes classifier based on them: the Qualitative Bayes Classifier. This classification method keeps Naive-Bayes accuracy while gaining interpretability, so we think it can be useful for the Data Mining step of the Knowledge Discovery process.


  1. 1.
    Christopher Elsaesser. Explanation of probabilistic inference. In L. N. Kanal, T. S. Levitt, and J. F. Lemmer, editors, Uncertainty in Artificial Intelligence 3, pages 387–400. Elsevier Science Publishers B.V. (North-Holland), 1989.Google Scholar
  2. 2.
    Jerome H. Friedman. On Bias, Variance, 0/1-Loss, and the Curse-of-Dimensionality. Data Mining and Knowledge Discovery, 1:55–77, 1997.CrossRefGoogle Scholar
  3. 3.
    Edgard M. Johnson. Numerical encoding of qualitative expressions of uncertainty. Technical Report 250, Army Research Institute for the Behavioural and Social Sciences, Arlington, Virginia, 1973.Google Scholar
  4. 4.
    R. Kohavi, G. John, R. Long, D. Manley, and K. Pfleger. MLC++: A machine learning library in C++. In Tools with Artificial Intelligence, pages 740–743. IEEE Computer Society Press, 1994.Google Scholar
  5. 5.
    Pat Langley, Wayne Iba, and Kevin Thompson. An Analysis of Bayesian Classifiers. In Proceedings of the Tenth National Conference on Artificial Intelligence, pages 223–228. AAAI Press and MIT Press, 1992.Google Scholar
  6. 6.
    S. Lichtenstein and J. R. Newman. Empirical scaling of common verbal phrases associated with numerical probabilities. Psychon. Sci., 9(10), 1967.Google Scholar
  7. 7.
    Eric Neufeld. A probabilistic commonsense reasoner. International Journal of Intelligent Systems, 5:565–594, 1990.MATHGoogle Scholar
  8. 8.
    G. C. Oden: Integration of fuzzy logical information. Journal of Experimental Psychology: Human Perception and Performance, 3(4):565–575, 1977.MathSciNetCrossRefGoogle Scholar
  9. 9.
    Simon Parsons. Further results in qualitative uncertainty. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 3(2):187–210, 1995.MathSciNetCrossRefGoogle Scholar
  10. 10.
    George Polya. Mathematics and Plausible Reasoning, Vol. II: Patterns of Plausible Inference. Princeton, New Jersey: Princeton University Press, 1954.Google Scholar
  11. 11.
    L. Trave and N. Piera. The orders of magnitude models as qualitative algebras. In 11th IJCAI, Detroit, 1989.Google Scholar
  12. 12.
    T. S. Wallsten, D. V. Budescu, A. Rapoport, R. Zwick, and B. Forsyth. Measuring the vague meanings of probability terms. Technical Report 173, The L. L. Thurstone Psychometrich Laboratory, Chapel Hill, N.C., 1985.Google Scholar
  13. 13.
    Michael P. Wellman. Fundamental concepts of qualitative networks. Artificial Intelligence, 44:257–303, 1990.MATHMathSciNetCrossRefGoogle Scholar
  14. 14.
    Alf C. Zimmer. Verbal vs. numerical processing of subjective probabilities. In R. W. Scholz, editor, Decision Making Under Uncertainty, pages 159–182. Elsevier Science Publishers B.V. (North-Holland), 1983.Google Scholar
  15. 15.
    Alf C. Zimmer. The estimation of subjective probabilities via categorical judgments of uncertainty. In Proceedings of the Workshop on Uncertainty and Probability in Artificial Intelligence, pages 217–224. UCLA, 1985.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Jesús Cerquides
    • 1
  • Ramon López de Màntaras
    • 1
  1. 1.Artificial Intelligence Research Institute, IIIA Spanish Council for Scientific ResearchCSICBellaterra, BarcelonaSpain

Personalised recommendations