Learning Directed Probabilistic Logical Models: Ordering-Search Versus Structure-Search

Authors:
Daan Fierens;Jan Ramon;Maurice Bruynooghe;Hendrik Blockeel
Affiliations:
K.U.Leuven, Dept. of Computer Science, Celestijnenlaan 200A, 3001 Heverlee, Belgium;K.U.Leuven, Dept. of Computer Science, Celestijnenlaan 200A, 3001 Heverlee, Belgium;K.U.Leuven, Dept. of Computer Science, Celestijnenlaan 200A, 3001 Heverlee, Belgium;K.U.Leuven, Dept. of Computer Science, Celestijnenlaan 200A, 3001 Heverlee, Belgium
Venue:
ECML '07 Proceedings of the 18th European conference on Machine Learning
Year:
2007

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Abstract

We discuss how to learn non-recursive directed probabilistic logical models from relational data. This problem has been tackled before by upgrading the structure-search algorithm initially proposed for Bayesian networks. In this paper we propose to upgrade another algorithm, namely ordering-search, since for Bayesian networks this was found to work better than structure-search. We experimentally compare the two upgraded algorithms on two relational domains. We conclude that there is no significant difference between the two algorithms in terms of quality of the learnt models while ordering-search is significantly faster.