Combining clauses with various precisions and recalls to produce accurate probabilistic estimates

Authors:
Mark Goadrich;Jude Shavlik
Affiliations:
University of Wisconsin-Madison;University of Wisconsin-Madison
Venue:
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Year:
2007

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Abstract

Statistical Relational Learning (SRL) combines the benefits of probabilistic machine learning approaches with complex, structured domains from Inductive Logic Programming (ILP). We propose a new SRL algorithm, GleanerSRL, to generate the probability that an example is positive within highly-skewed relational domains. In this work, we combine clauses from Gleaner, an ILP algorithm for learning a wide variety of first-order clauses, with the propositional learning technique of support vector machines to learn well-calibrated probabilities. We find that our results are comparable to SRL algorithms SAYU and SAYUVISTA on a well-known relational testbed.