Machine Learning

, Volume 83, Issue 2, pp 219–239 | Cite as

Relational information gain

  • Marco Lippi
  • Manfred Jaeger
  • Paolo Frasconi
  • Andrea Passerini
Article

Abstract

We introduce relational information gain, a refinement scoring function measuring the informativeness of newly introduced variables. The gain can be interpreted as a conditional entropy in a well-defined sense and can be efficiently approximately computed. In conjunction with simple greedy general-to-specific search algorithms such as FOIL, it yields an efficient and competitive algorithm in terms of predictive accuracy and compactness of the learned theory. In conjunction with the decision tree learner TILDE, it offers a beneficial alternative to lookahead, achieving similar performance while significantly reducing the number of evaluated literals.

Keywords

Relational learning Inductive logic programming Information gain 

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Copyright information

© The Author(s) 2010

Authors and Affiliations

  • Marco Lippi
    • 1
  • Manfred Jaeger
    • 2
  • Paolo Frasconi
    • 1
  • Andrea Passerini
    • 3
  1. 1.Dipartimento di Sistemi e InformaticaUniversità degli Studi di FirenzeFlorenceItaly
  2. 2.Department for Computer ScienceAalborg UniversityAalborgDenmark
  3. 3.Dipartimento di Ingegneria e Scienza dell’InformazioneUniversità degli Studi di TrentoTrentoItaly

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