Refining Aggregate Conditions in Relational Learning

Celine Vens, Jan Ramon, Hendrik Blockeel

Abstract: In relational learning, predictions for an individual are based not only on its own properties but also on the properties of a set of related individuals. Many systems use aggregates to summarize this set. Features thus introduced compare the result of an aggregate function to a threshold. We consider the case where the set to be aggregated is generated by a complex query and present a framework for refining such complex aggregate conditions along three dimensions: the aggregate function, the query used to generate the set, and the threshold value. The proposed aggregate refinement operator allows a more efficient search through the hypothesis space and thus can be beneficial for many relational learners that use aggregates. As an example application, we have implemented the refinement operator in a relational decision tree induction system. Experimental results show a significant efficiency gain in comparison with the use of a less advanced refinement operator.