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Mixed Decision Trees: An Evolutionary Approach

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Data Warehousing and Knowledge Discovery (DaWaK 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4081))

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Abstract

In the paper, a new evolutionary algorithm (EA) for mixed tree learning is proposed. In non-terminal nodes of a mixed decision tree different types of tests can be placed, ranging from a typical univariate inequality test up to a multivariate test based on a splitting hyperplane. In contrast to classical top-down methods, our system searches for an optimal tree in a global manner, i.e. it learns a tree structure and tests in one run of the EA. Specialized genetic operators allow for generating new sub-trees, pruning existing ones as well as changing the node type and the tests. The proposed approach was experimentally verified on both artificial and real-life data and preliminary results are promising.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science, Białystok Technical University, Wiejska 45a, 15-351, Białystok, Poland

    Marek Krȩtowski & Marek Grześ

Authors
  1. Marek Krȩtowski
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  2. Marek Grześ
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Editor information

Editors and Affiliations

  1. Institute of Software Technology and Interactive Systems, Vienna University of Technology, Favoritenstr. 9-11/188, A-1040, Wien, Austria

    A Min Tjoa

  2. Department of Software and Computing Systems, University of Alicante, Spain

    Juan Trujillo

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© 2006 Springer-Verlag Berlin Heidelberg

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Krȩtowski, M., Grześ, M. (2006). Mixed Decision Trees: An Evolutionary Approach. In: Tjoa, A.M., Trujillo, J. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2006. Lecture Notes in Computer Science, vol 4081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11823728_25

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  • DOI: https://doi.org/10.1007/11823728_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37736-8

  • Online ISBN: 978-3-540-37737-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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