Knowledge discovery through symbolic regression with heuristiclab

  • Authors:

  • Gabriel Kronberger;Stefan Wagner;Michael Kommenda;Andreas Beham;Andreas Scheibenpflug;Michael Affenzeller

  • Affiliations:

  • School for Informatics, Communication and Media, University of Applied Sciences Upper Austria, Austria;School for Informatics, Communication and Media, University of Applied Sciences Upper Austria, Austria;School for Informatics, Communication and Media, University of Applied Sciences Upper Austria, Austria;School for Informatics, Communication and Media, University of Applied Sciences Upper Austria, Austria;School for Informatics, Communication and Media, University of Applied Sciences Upper Austria, Austria;School for Informatics, Communication and Media, University of Applied Sciences Upper Austria, Austria

  • Venue:
  • ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
  • Year:
  • 2012

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Abstract

This contribution describes how symbolic regression can be used for knowledge discovery with the open-source software HeuristicLab. HeuristicLab includes a large set of algorithms and problems for combinatorial optimization and for regression and classification, including symbolic regression with genetic programming. It provides a rich GUI to analyze and compare algorithms and identified models. This contribution mainly focuses on specific aspects of symbolic regression that are unique to HeuristicLab, in particular, the identification of relevant variables and model simplification.