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 SAYU-VISTA on a well-known relational testbed.
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Goadrich, M., Shavlik, J. (2008). Combining Clauses with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates. 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_15
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DOI: https://doi.org/10.1007/978-3-540-78469-2_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78468-5
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