Skip to main content
Springer Nature Link
Log in

Revision of First-Order Bayesian Classifiers

  • Conference paper
  • First Online:
Inductive Logic Programming (ILP 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2583))

Included in the following conference series:

Abstract

New representation languages that integrate first order logic with Bayesian networks have been proposed in the literature. Probabilistic Relational models (PRM) and Bayesian Logic Programs (BLP) are examples. Algorithms to learn both the qualitative and the quantitative components of these languages have been developed. Recently, we have developed an algorithm to revise a BLP. In this paper, we discuss the relationship among these approaches, extend our revision algorithm to return the highest probabilistic scoring BLP and argue that for a classi- fication task our approach, which uses techniques of theory revision and so searches a smaller hypotheses space, can be a more adequate choice.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. J. Binder, D. Koller, S. Russell and others. Adaptive probabilistic networks with hidden variables. Machine Learining. pp.213–244, 1997.

    Google Scholar 

  2. C.A. Brunk, 1996. An Investigation of knowledge Intensive Approaches to Concept Leaning and Theory Refinement. Ph.D. thesis, University of California, Irvine, CA.

    Google Scholar 

  3. D.M. Chickering. Learnig Bayesian networks is NP-complete. In: Learning from Data: Artifial Intelligence and Statiscs V. D. Fisher and H.-J. Lenz. Spring Verlag, pp.121–130, 1996.

    Google Scholar 

  4. N. Friedman, I. Nachman and D. Peér. Learning Bayesian Network Structure from Massive Datasets: The Sparse Candidate Algorithm. In Proceedings of the Fifteenth Conference on Uncertainty in Articial Intelligence. pp.206–215, 1999.

    Google Scholar 

  5. I. Fabian and D. A. Lambert. First-Order Bayesian Reasoning. Proceedings Eleventh Australian Joint Conference on Artificial Intelligence. 1502, 131–142, 1998.

    Google Scholar 

  6. N. Friedman, L. Getoor, D. Koller and A. Pfeffer. Learning Probabilistic Relational Models. Proc. IJCAI-99, 1300–1309, 1999.

    Google Scholar 

  7. P. Haddawy. An overview of some recent developments on Bayesian problem soving techniques. AI Magazine, Spring 1999-Special issue on Uncertainty in AI, 1999.

    Google Scholar 

  8. D. Heckerman. A tutorial on learning with Bayesian networks. In M.I. Jordan, editor, Learning in Graphical Models, pp 301–354. MIT Press Cambridge, MA, 1998.

    Google Scholar 

  9. M. Jaeger. Relational Bayesian Networks. Proc. 13th Conference on Uncertainty in AI. pp.266–273. Morgan Kaufmann, 1997.

    Google Scholar 

  10. K. Kersting, L. De Raedt and S. Kramer. Interpreting Bayesian Logic Programs. In Working Notes of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data (SRL), Austin, Texas, 2000.

    Google Scholar 

  11. K. Kersting and L. De Raedt. Bayesian Logic Programs. Proc. of the Work-in-Progress Track at the 10th International Conference on Inductive Logic Programming, 2000.

    Google Scholar 

  12. K. Kersting and L. De Raedt. Bayesian Logic Programs. Technical Report 151, University of Freiburg, Institute for COmputer Science, Abril 2001.

    Google Scholar 

  13. K. Kersting and L. De Raedt. Towards Combining Inductive Logic Programming with Bayesian Networks. In proceedings of the Eleventh International Conference of Inductive Logic Programming. Strasbourg, France, pp. 118–131, 2001.

    Google Scholar 

  14. K. Kersting and L. De Raedt. Adaptive Bayesian Logic Programs. In proceedings of the Eleventh International Conference of Inductive Logic Programming. Strasbourg, France, pp. 104–117, 2001.

    Google Scholar 

  15. D. Koller. Probabilistic Relational Models. In Proc. of the 9th Int. Workshop on ILP. LNAI 1634, 3–13, Springer Verlag, 1999.

    Google Scholar 

  16. D. Koller and A. Pfeffer. Learning probabilities for noisy first-order rules. In Proc. of the 15th International Joint Conference on Artficial Intelligence (IJCAI-97), 1316–1323, 1997.

    Google Scholar 

  17. W. Lam and F. Bacchus. Learning Bayesian belief networks: An approach based on the mdl principle. Computatinal Intelligence, 10(3),pp. 269–29, 1994.

    Article  Google Scholar 

  18. S.L Lauritzen. The Em algorithm for graphical association models with missing data. Computational Statistics and Data Analysis, 19: 191–201, 1995.

    Article  MATH  Google Scholar 

  19. J. Lloyd. Foundations of Logic Programming. Springer Verlag, 2. edition, 1989.

    Google Scholar 

  20. S. Muggleton. Stochastic logic programs. In L.De Raedt, editor, Advances in Inductive Logic Programming. IOS Press, 1996.

    Google Scholar 

  21. G.J. McLachlan and T. Krishnan. The EM algorithm and Extensions. Wiley Interscience, 1997.

    Google Scholar 

  22. T. Mitchell. Machine Learning. McGraw-Hill New York, NY, 1997.

    MATH  Google Scholar 

  23. L. Ngo and P. Haddawy. Answering queries from context-sensitive probabilistic knowledge bases. Theoretical Computer Science, 171:147–177, 1997.

    Article  MATH  MathSciNet  Google Scholar 

  24. J. Pearl. Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufman, 2. edition, 1991.

    Google Scholar 

  25. D. Poole. Probabilistic horn abduction and bayesian networks. Artificial Intelligence, 64(1): 81–129, 1993.

    Article  MATH  Google Scholar 

  26. D. Poole. Abducing Through Negation as Failure: Stable models within the independent choice logic. Journal of Logic Programming, 44: 5–359, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  27. D. Poole. Learning, Bayesian Probability, Graphical Models, and Abduction. P. Flach and A. Kakas, editors, Abduction and Induction: essays on their relation and integration. Kluwer, 1998.

    Google Scholar 

  28. S. Ramachandran and R. Mooney. Theory Refnement of Bayesian Networds with Hidden Variables. 15th International Conference on Machine Learning (ICML), pp.454–462, Morgan Kaufman, 1998.

    Google Scholar 

  29. K. Revoredo and G. Zaverucha. Theory Refinement of Bayesian Logic Programs. Proceedings of the Eigth International Conference on Neural Information Processing (ICONIP), Shanghai, China. pp. 1088–1092, 2001.

    Google Scholar 

  30. B. Richards and R. Mooney. Automated Refinement of First-Order Horn-Clause Domain Theories. Machine Learning 19, pp. 95–131, 1995.

    Google Scholar 

  31. S. Wrobel. First-order Theory Refinement. Advances in Inductive Logic Programming, edited by Luc de Raedt, pp. 14–33, IOS Press, 1996.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Programa de Engenharia de Sistemas e ComputaÇão-COPPE, Universidade Federal do Rio de Janeiro, Caixa Postal 68511, 21945-970, Rio de Janeiro RJ, Brasil

    Kate Revoredo & Gerson Zaverucha

Authors
  1. Kate Revoredo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gerson Zaverucha
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Information Technology and Engineering, University of Ottawa, 800 King Edward Ave., K1N 6N5, Ottawa, ON, Canada

    Stan Matwin

  2. School of Computer Science and Engineering, University of New South Wales, 2052, Sydney, NSW, Australia

    Claude Sammut

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Revoredo, K., Zaverucha, G. (2003). Revision of First-Order Bayesian Classifiers. In: Matwin, S., Sammut, C. (eds) Inductive Logic Programming. ILP 2002. Lecture Notes in Computer Science(), vol 2583. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36468-4_15

Download citation

  • DOI: https://doi.org/10.1007/3-540-36468-4_15

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00567-4

  • Online ISBN: 978-3-540-36468-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics