Abstract
Stochastically searching the space of candidate clauses is an appealing way to scale up ILP to large datasets. We address an approach that uses a Bayesian network model to adaptively guide search in this space. We examine guiding search towards areas that previously performed well and towards areas that ILP has not yet thoroughly explored. We show improvement in area under the curve for recall-precision curves using these modifications.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Boyan, J., Moore, A.: Learning Evaluation Functions for Global Optimization and Boolean Satisfiability. In: Proceedings of the Fifteenth National Conference on Artificial Intelligence, pp. 3–10 (1998)
DiMaio, F., Shavlik, J.: Learning an Approximation to Inductive Logic Programming Clause Evaluation. In: Proceedings of the 14th International Conference on Inductive Logic Programming, Porto, Portugal, pp. 80–97 (2004)
Džeroski, S., Lavrac, N.: An Introduction to Inductive Logic Programming. In: Džeroski, S., Lavrac, N. (eds.) Proceedings of Relational Data Mining, pp. 48–66. Springer, Heidelberg (2001)
Goadrich, M., Oliphant, L., Shavlik, J.: Gleaner: Creating Ensembles of First-Order Clauses to Improve Recall-Precision Curves. Machine Learning 64(1-3), 231–261 (2006)
Heckerman, D.: A Tutorial on Learning with Bayesian Networks. Technical Report MSR-TR-95-06, Microsoft Research, Redmond, Washington, (revised June 1996)
Muggleton, S.: Inverse Entailment and Progol. New Generation Computing Journal 13, 245–286 (1995)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc. San Francisco (1988)
Pelikan, M., Goldberg, D., Cantú-Paz, E.: BOA: The Bayesian Optimization Algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, FL, vol. I, pp. 525–532. Morgan Kaufmann Publishers, San Francisco (1999)
Ray, S., Craven, M.: Representing Sentence Structure in Hidden Markov Models for Information Extraction. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence (2001)
Richardson, M., Domingos, P.: Markov Logic Networks. Machine Learning 62(1-2), 107–136 (2006)
Rubinstein, R., Kroese, D.: The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization. In: Monte-Carlo Simulation and Machine Learning, Springer, Secaucus (2004)
Srinivasan, A.: The Aleph Manual Version 4. (2003), http://web.comlab.ox.ac.uk/oucl/research/areas/machlearn/Aleph/
Železný, F., Srinivasan, A., Page, D.: Lattice-Search Runtime Distributions be Heavy-Tailed. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS (LNAI), vol. 2583, pp. 333–345. Springer, Heidelberg (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Oliphant, L., Shavlik, J. (2008). Using Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming. In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds) Inductive Logic Programming. ILP 2007. Lecture Notes in Computer Science(), vol 4894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78469-2_20
Download citation
DOI: https://doi.org/10.1007/978-3-540-78469-2_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78468-5
Online ISBN: 978-3-540-78469-2
eBook Packages: Computer ScienceComputer Science (R0)