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An Evolutionary Algorithm for Global Induction of Regression Trees

  • Marek Krȩtowski
  • Marcin Czajkowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6114)

Abstract

In the paper a new evolutionary algorithm for induction of univariate regression trees is proposed. In contrast to typical top-down approaches it globally searches for the best tree structure and tests in internal nodes. The population of initial trees is created with diverse top-down methods on randomly chosen sub-samples of the training data. Specialized genetic operators allow the algorithm to efficiently evolve regression trees. The complexity term introduced in the fitness function helps to mitigate the over-fitting problem. The preliminary experimental validation is promising as the resulting trees can be significantly less complex with at least comparable performance to the classical top-down counterpart.

Keywords

Root Mean Square Error Evolutionary Algorithm Regression Tree Internal Node Global Induction 
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 2010

Authors and Affiliations

  • Marek Krȩtowski
    • 1
  • Marcin Czajkowski
    • 1
  1. 1.Faculty of Computer ScienceBialystok University of TechnologyBiałystokPoland

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