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Refining Aggregate Conditions in Relational Learning

  • Celine Vens
  • Jan Ramon
  • Hendrik Blockeel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)

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.

Keywords

Generalize Average Monotonicity Property Start Condition Aggregate Condition Inductive Logic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Celine Vens
    • 1
  • Jan Ramon
    • 1
  • Hendrik Blockeel
    • 1
  1. 1.Department of Computer ScienceKatholieke Universiteit LeuvenLeuvenBelgium

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