Using Bayesian networks for selecting classifiers in GP ensembles

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

@InProceedings{DeStefano:2011:GECCOcomp,
  author =       "Claudio {De Stefano} and Gianluigi Folino and 
                 Francesco Fontanella and Alessandra {Scotto di Freca}",
  title =        "Using {Bayesian} networks for selecting classifiers in
                 {GP} ensembles",
  booktitle =    "GECCO '11: Proceedings of the 13th annual conference
                 companion on Genetic and evolutionary computation",
  year =         "2011",
  editor =       "Natalio Krasnogor and Pier Luca Lanzi and 
                 Andries Engelbrecht and David Pelta and Carlos Gershenson and 
                 Giovanni Squillero and Alex Freitas and 
                 Marylyn Ritchie and Mike Preuss and Christian Gagne and 
                 Yew Soon Ong and Guenther Raidl and Marcus Gallager and 
                 Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and 
                 Nikolaus Hansen and Silja Meyer-Nieberg and 
                 Jim Smith and Gus Eiben and Ester Bernado-Mansilla and 
                 Will Browne and Lee Spector and Tina Yu and Jeff Clune and 
                 Greg Hornby and Man-Leung Wong and Pierre Collet and 
                 Steve Gustafson and Jean-Paul Watson and 
                 Moshe Sipper and Simon Poulding and Gabriela Ochoa and 
                 Marc Schoenauer and Carsten Witt and Anne Auger",
  isbn13 =       "978-1-4503-0690-4",
  keywords =     "genetic algorithms, genetic programming, Genetics
                 based machine learning: Poster",
  pages =        "173--174",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001858.2001955",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "In this paper we present a novel approach for
                 combining GP-based ensembles by means of a Bayesian
                 Network. The proposed system is able to effectively
                 learn decision tree ensembles using two different
                 strategies: decision trees ensembles are learnt by
                 means of boosted GP algorithm; the responses of the
                 learned ensembles are combined using a Bayesian
                 network, which also implements a selection strategy
                 that reduces the size of the built ensembles.",
  notes =        "Also known as \cite{2001955} Distributed on CD-ROM at
                 GECCO-2011.

                 ACM Order Number 910112.",
}

Genetic Programming entries for Claudio De Stefano Gianluigi Folino Francesco R Fontanella Alessandra Scotto di Freca

Citations