Feature Discovery with Type Extension Trees

  • Paolo Frasconi
  • Manfred Jaeger
  • Andrea Passerini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5194)


We are interested in learning complex combinatorial features from relational data. We rely on an expressive and general representation language whose semantics allows us to express many features that have been used in different statistical relational learning settings. To avoid expensive exhaustive search over the space of relational features, we introduce a heuristic search algorithm guided by a generalized relational notion of information gain and a discriminant function. The algorithm succesfully finds interesting and interpretable features on artificial and real-world relational learning problems.


Discriminant Function Class Label Feature Discovery Metal Binding Site Inductive Logic Programming 
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 2008

Authors and Affiliations

  • Paolo Frasconi
    • 2
  • Manfred Jaeger
    • 1
  • Andrea Passerini
    • 2
  1. 1.Department for Computer ScienceAalborg UniversityDenmark
  2. 2.Dipartimento di Sistemi e InformaticaUniversitá degli Studi di FirenzeItaly

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