GP ensembles for large-scale data classification

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

  author =       "Gianluigi Folino and Clara Pizzuti and 
                 Giandomenico Spezzano",
  title =        "GP ensembles for large-scale data classification",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2006",
  volume =       "10",
  number =       "5",
  pages =        "604--616",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, Bagging,
                 boosting, classification, data mining",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2005.863627",
  size =         "13 pages",
  abstract =     "An extension of cellular genetic programming for data
                 classification (CGPC) to induce an ensemble of
                 predictors is presented. Two algorithms implementing
                 the bagging and boosting techniques are described and
                 compared with CGPC. The approach is able to deal with
                 large data sets that do not fit in main memory since
                 each classifier is trained on a subset of the overall
                 training data. The predictors are then combined to
                 classify new tuples. Experiments on several data sets
                 show that, by using a training set of reduced size,
                 better classification accuracy can be obtained, but at
                 a much lower computational cost",
  notes =        "Also known as \cite{1705406}",

Genetic Programming entries for Gianluigi Folino Clara Pizzuti Giandomenico Spezzano