Probabilistic rule learning

Authors:
Luc De Raedt;Ingo Thon
Affiliations:
Department of Computer Science, Katholieke Universiteit Leuven, Belgium;Department of Computer Science, Katholieke Universiteit Leuven, Belgium
Venue:
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Year:
2010

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Abstract

Traditionally, rule learners have learned deterministic rules from deterministic data, that is, the rules have been expressed as logical statements and also the examples and their classification have been purely logical. We upgrade rule learning to a probabilistic setting, in which both the examples themselves as well as their classification can be probabilistic. The setting is incorporated in the probabilistic rule learner ProbFOIL, which combines the principles of the relational rule learner FOIL with the probabilistic Prolog, ProbLog. We report also on some experiments that demonstrate the utility of the approach.