Pruning GP-Based Classifier Ensembles by Bayesian Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Claudio {De Stefano} and Gianluigi Folino and 
                 Francesco Fontanella and Alessandra {Scotto di Freca}",
  title =        "Pruning {GP}-Based Classifier Ensembles by {Bayesian}
  booktitle =    "Parallel Problem Solving from Nature, PPSN XII (part
  year =         "2012",
  editor =       "Carlos A. {Coello Coello} and Vincenzo Cutello and 
                 Kalyanmoy Deb and Stephanie Forrest and 
                 Giuseppe Nicosia and Mario Pavone",
  volume =       "7491",
  series =       "Lecture Notes in Computer Science",
  pages =        "236--245",
  address =      "Taormina, Italy",
  month =        sep # " 1-5",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-32936-4",
  DOI =          "doi:10.1007/978-3-642-32937-1_24",
  size =         "10 pages",
  abstract =     "Classifier ensemble techniques are effectively used to
                 combine the responses provided by a set of classifiers.
                 Classifier ensembles improve the performance of single
                 classifier systems, even if a large number of
                 classifiers is often required. This implies large
                 memory requirements and slow speeds of classification,
                 making their use critical in some applications. This
                 problem can be reduced by selecting a fraction of the
                 classifiers from the original ensemble. In this work,
                 it is presented an ensemble-based framework that copes
                 with large datasets, however selecting a small number
                 of classifiers composing the ensemble. The framework is
                 based on two modules: an ensemble-based Genetic
                 Programming (GP) system, which produces a high
                 performing ensemble of decision tree classifiers, and a
                 Bayesian Network (BN) approach to perform classifier
                 selection. The proposed system exploits the advantages
                 provided by both techniques and allows to strongly
                 reduce the number of classifiers in the ensemble.
                 Experimental results compare the system with well-known
                 techniques both in the field of GP and BN and show the
                 effectiveness of the devised approach. In addition, a
                 comparison with a pareto optimal strategy of pruning
                 has been performed.",
  bibsource =    "DBLP,",
  affiliation =  "Universita di Cassino e del Lazio Meridionale, Italy",

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