A Bayesian Approach for Combining Ensembles of GP Classifiers

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

  author =       "C. {De Stefano} and F. Fontanella and G. Folino and 
                 A. Scotto {di Freca}",
  title =        "A {Bayesian} Approach for Combining Ensembles of {GP}
  booktitle =    "Multiple Classifier Systems",
  year =         "2011",
  editor =       "Carlo Sansone and Josef Kittler and Fabio Roli",
  volume =       "6713",
  series =       "LNCS",
  pages =        "26--35",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-21557-5",
  DOI =          "doi:10.1007/978-3-642-21557-5_5,",
  size =         "10 pages",
  abstract =     "Recently, ensemble techniques have also attracted the
                 attention of Genetic Programming (GP) researchers. The
                 goal is to further improve GP classification
                 performances. Among the ensemble techniques, also
                 bagging and boosting have been taken into account.
                 These techniques improve classification accuracy by
                 combining the responses of different classifiers by
                 using a majority vote rule. However, it is really hard
                 to ensure that classifiers in the ensemble be
                 appropriately diverse, so as to avoid correlated
                 errors. Our approach tries to cope with this problem,
                 designing a framework for effectively combine GP-based
                 ensemble by means of a Bayesian Network. The proposed
                 system uses two different approaches. The first one
                 applies a boosting technique to a GP-based
                 classification algorithm in order to generate an
                 effective decision trees ensemble. The second module
                 uses a Bayesian network for combining the responses
                 provided by such ensemble and select the most
                 appropriate decision trees. The Bayesian network is
                 learned by means of a specifically devised Evolutionary
                 algorithm. Preliminary experimental results confirmed
                 the effectiveness of the proposed approach.",

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