Acquiring and validating background knowledge for machine learning using function decomposition

  • Blaž Zupan
  • Sašo Džeroski
Knowledge Acquisition and Learning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1211)


Domain or background knowledge is often needed in order to solve difficult problems of learning medical diagnostic rules. Earlier experiments have demonstrated the utility of background knowledge when learning rules for early diagnosis of rheumatic diseases. A particular form of background knowledge comprising typical co-occurrences of several groups of attributes was provided by a medical expert. This paper explores the possibility to automate the process of acquiring background knowledge of this kind. A method based on function decomposition is proposed that identifies typical co-occurrences for a given set of attributes. The method is evaluated by comparing the typical co-occurrences it identifies, as well as their contribution to the performance of machine learning algorithms, to the ones provided by a medical expert.


Mutual Information Rheumatic Disease Background Knowledge Morning Stiffness Medical Expert 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Ashenhurst, R.L. (1952). The decomposition of switching functions. Technical report, Bell Laboratories BL-1(11): 541–602.Google Scholar
  2. 2.
    Biermann, A.W., Fairfield, J., and Beres, T. (1982). Signature table systems and learning. IEEE Trans. Syst. Man Cybern., 12(5): 635–648.Google Scholar
  3. 3.
    Clark, P., and Boswell, R. (1991). Rule induction with CN2: Some recent improvements. In Proc. Fifth European Working Session on Learning, pages 151–163. Springer, Berlin.Google Scholar
  4. 4.
    Cover, T.M., and Hart, P.E. (1968). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13: 21–27.Google Scholar
  5. 5.
    Curtis, H.A. (1962). A New Approach to the Design of Switching Functions. Van Nostrand, Princeton, N.J.Google Scholar
  6. 6.
    Džeroski, S., Cestnik, B., and Petrovski, I. (1993). Using the m-estimate in rule induction. Journal of Computing and Information Technology, 1: 37–46.Google Scholar
  7. 7.
    Džeroski, S., and Lavrač, N. (1996). Rule induction and instance-based learning applied in medical diagnosis. Technology and Health Care.Google Scholar
  8. 8.
    Fix, E., and Hodges, J.L. (1957). Discriminatory analysis. Nonparametric discrimination. Consistency properties. Technical Report 4, US Air Force School of Aviation Medicine. Randolph Field, TX.Google Scholar
  9. 9.
    Harmon, P., Maus, R., and Morrissey, W. (1988). Expert systems: Tools & Applications. John Wiley, New York.Google Scholar
  10. 10.
    Karalič, A., and Pirnat, V. (1990). Machine learning in rheumatology. Sistemica 1(2): 113–123.Google Scholar
  11. 11.
    Kononenko, I., and Bratko, I. (1991). Information-based evaluation criterion for classifier's performance. Machine Learning, 6(1): 67–80.Google Scholar
  12. 12.
    Lavrač, N., and Džeroski, S. (1994). Inductive Logic Programming: Techniques and Applications. Ellis Horwood, Chichester.Google Scholar
  13. 13.
    Lavrač, N., Džeroski, S., Pirnat, V., and Križman, V. (1991). Learning rules for early diagnosis of rheumatic diseases. In Proc. Third Scandinavian Conference on Artificial Intelligence, pages 138–149. IOS Press, Amsterdam.Google Scholar
  14. 14.
    Lavrač, N., Džeroski, S., Pirnat, V., and Križman, V. (1993). The utility of background knowledge in learning medical diagnostic rules. Applied Artificial Intelligence, 7:273–293.Google Scholar
  15. 15.
    Perkowski, M.A., and Grygiel, S. (1995). A survey of literature on function decomposition. Techical report, Dept. of Electrical Engineering, Portland State University.Google Scholar
  16. 16.
    Pirnat, V., Kononenko, I., Janc, T., and Bratko, I. (1989). Medical analysis of automatically induced rules. In Proc. 2nd European Conference on Artificial Intelligence in Medicine, pages 24–36. Springer, Berlin.Google Scholar
  17. 17.
    Samuel, A. (1967). Some studies in machine learning using the game of checkers II: Recent progress. IBM J. Res. Develop., 11:601–617.Google Scholar
  18. 18.
    Shannon, C.E. (1948). A mathematical theory of communication. Bell. Syst. Techn. J., 27: 379–423.Google Scholar
  19. 19.
    Wettschereck, D. (1994). A study of distance-based machine learning algorithms. PhD Thesis, Department of Computer Science, Oregon State University, Corvallis, OR.Google Scholar
  20. 20.
    Zupan, B., and Bohanec, M. (1996). Learning concept hierarchies from examples by function decomposition. Technical report, IJSDP-7455, J. Stefan Institute, Ljubljana.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Blaž Zupan
    • 1
  • Sašo Džeroski
    • 1
  1. 1.Department of Intelligent SystemsJožef Stefan InstituteLjubljanaSlovenia

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