Inducing Diverse Decision Forests with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{eurogp:SuchyK05,
  author =       "Jan Suchy and Jiri Kubalik",
  editor =       "Maarten Keijzer and Andrea Tettamanzi and 
                 Pierre Collet and Jano I. {van Hemert} and Marco Tomassini",
  title =        "Inducing Diverse Decision Forests with Genetic
                 Programming",
  booktitle =    "Proceedings of the 8th European Conference on Genetic
                 Programming",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3447",
  year =         "2005",
  address =      "Lausanne, Switzerland",
  month =        "30 " # mar # " - 1 " # apr,
  organisation = "EvoNet",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-25436-6",
  pages =        "301--310",
  URL =          "http://ida.felk.cvut.cz/cgi-bin/docarc/public.pl/document/6/suchy05eurogp.pdf",
  DOI =          "doi:10.1007/b107383",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  abstract =     "This paper presents an algorithm for induction of
                 ensembles of decision trees, also referred to as
                 decision forests. In order to achieve high
                 expressiveness the trees induced are multivariate, with
                 various, possibly user-defined tests in their internal
                 nodes. Strongly typed genetic programming is used to
                 evolve structure of the tests. Special attention is
                 given to the problem of diversity of the forest
                 constructed. An approach is proposed, which explicitly
                 encourages the induction algorithm to produce a
                 different tree each run, which represents an
                 alternative description of the data. It is shown that
                 forests constructed with this approach have
                 significantly reduced classification error even for
                 small forest size, compared to other ensemble methods.
                 Classification accuracy of the algorithm is also
                 compared to other recent methods on several real-world
                 datasets.",
  notes =        "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
                 conjunction with EvoCOP2005 and EvoWorkshops2005",
}

Genetic Programming entries for Jan Suchy Jiri Kubalik

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