On multi-class problems and discretization in inductive logic programming

  • Wim Van Laer
  • Luc De Raedt
  • Sago Dzeroski
Communications Session 3B Learning and Discovery Systems
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1325)


In practical applications of machine learning and knowledge discovery, handling multi-class problems and real numbers are important issues. While attribute-value learners address these problems as a rule, very few ILP systems do so. The few ILP systems that handle real numbers mostly do so by trying out all real values applicable, thus running into efficiency or overfitting problems.

The ILP learner ICL (Inductive Constraint Logic, learns first order logic formulae from positive and negative examples. The main characteristic of ICL is its view on examples, which are seen as interpretations which are true or false for the target theory. The paper reports on the extensions of ICL to tackle multi-class problems and real numbers. We also discuss some issues on learning CNF formulae versus DNF formulae related to these extensions. Finally, we present experiments in the practical domains of predicting mutagenesis, finite element mesh design and predicting biodegradability of chemical compounds.


Learning Knowledge Discovery Inductive Logic Programming Classification Discretization 


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  1. 1.
    H. Blockeel and L. De Raedt. Experiments with top-down induction of logical decision trees. Technical Report CW 247, Dept. of Computer Science, K.U.Leuven, January 1997.Google Scholar
  2. 2.
    J. Catlett. On changing continuous attributes into ordered discrete attributes. In Yves Kodratof, editor, Proceedings of the 5th European Working Session on Learning, volume 482 of Lecture Notes in Artificial Intelligence, pages 164–178. Springer-Verlag, 1991.Google Scholar
  3. 3.
    P. Clark and R. Boswell. Rule induction with CN2: Some recent improvements. In Yves Kodratof, editor, Proceedings of the 5th European Working Session on Learning, volume 482 of Lecture Notes in Artificial Intelligence, pages 151–163. Springer-Verlag, 1991.Google Scholar
  4. 4.
    L. De Raedt. Induction in logic. In R.S. Michalski and Wnek J., editors, Proceedings of the 3rd International Workshop on Multistrategy Learning, pages 29–38, 1996.Google Scholar
  5. 5.
    L. De Raedt and L. Dehaspe. Clausal discovery. Machine Learning, 26:99–146, 1997.Google Scholar
  6. 6.
    L. De Raedt and S. Dzeroski. First order jk-Causal theories are PAC-learnable. Artificial Intelligence, 70:375–392, 1994.Google Scholar
  7. 7.
    L. De Raedt and W. Van Laer. Inductive constraint logic. In Proceedings of the 5th Workshop on Algorithmic Learning Theory, volume 997 of Lecture Notes in Artificial Intelligence. Springer-Verlag, 1995.Google Scholar
  8. 8.
    B. Dolsak and S. Muggleton. The application of Inductive Logic Programming to finite element mesh design. In S. Muggleton, editor, Inductive logic programming, pages 453–472. Academic Press, 1992.Google Scholar
  9. 9.
    J. Dougherty, R. Kohavi, and M. Sahami. Supervised and unsupervised discretization of continuous features. In A. Prieditis and S. Russell, editors, Proc. Twelfth International Conference on Machine Learning. Morgan Kaufmann, 1995.Google Scholar
  10. 10.
    S. Dzeroski, B. Kompare, and W. Van Laer. Predicting biodegradability from chemical structure using ILP. Submitted.Google Scholar
  11. 11.
    U.M. Fayyad and K.B. Irani. Multi-interval discretization of continuous-valued attributes for classification learning. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, pages 1022–1027, San Mateo, CA, 1993. Morgan Kaufmann.Google Scholar
  12. 12.
    M. Genesereth and N. Nilsson. Logical foundations of artificial intelligence. Morgan Kaufmann, 1987.Google Scholar
  13. 13.
    D. Kazakov, L. Popelinsky, and O. Stepankova. ILP datasets page [http://www.gmd.de/ml-archive/datasets/ilp-res.html],1996.Google Scholar
  14. 14.
    J.W. Lloyd. Foundations of logic programming. Springer-Verlag, 2nd edition, 1987.Google Scholar
  15. 15.
    R.J. Mooney. Encouraging experimental results on learning cnf. Machine Learning, 19:79–92, 1995.Google Scholar
  16. 16.
    U. Pompe and I. Kononenko. Probabilistic first-order classification, 1997. Submitted.Google Scholar
  17. 17.
    A. Srinivasan, S.H. Muggleton, M.J.E. Sternberg, and R.D. King. Theories for mutagenicity: A study in first-order and feature-based induction. Artificial Intelligence, 85, 1996.Google Scholar
  18. 18.
    L. Valiant. A theory of the learnable. Communications of the ACM, 27:1134–1142, 1984.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Wim Van Laer
    • 1
  • Luc De Raedt
    • 1
  • Sago Dzeroski
    • 2
  1. 1.Department of Computer ScienceKatholieke Universiteit LeuvenHeverleeBelgium
  2. 2.Department of Intelligent SystemsJozef Stefan InstituteLjubljanaSlovenia

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