GP Ensembles for improving multi-class prediction problems

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Gianluigi Folino and Clara Pizzuti and 
                 Giandomenico Spezzano",
  title =        "GP Ensembles for improving multi-class prediction
  booktitle =    "AI*IA Workshop on Evolutionary Computation,
                 Evoluzionistico GSICE05",
  year =         "2005",
  editor =       "Sara Manzoni and Matteo Palmonari and Fabio Sartori",
  address =      "University of Milan Bicocca, Italy",
  month =        "20 " # sep,
  keywords =     "genetic algorithms, genetic programming, data mining,
                 classification, boosting",
  ISBN =         "88-900910-0-2",
  size =         "10 pages",
  abstract =     "Cellular Genetic Programming for data classification
                 extended with the boosting technique to induce an
                 ensemble of predictors is presented. The method
                 implements in parallel AdaBoost.M2 to efficiently deal
                 with multi-class problems and it is able to manage
                 large data sets that do not fit in main memory since
                 each classifier is trained on a subset of the overall
                 training data. Experiments on several data sets show
                 that, by using a training set of reduced size, better
                 classification accuracy can be obtained at a much lower
                 computational cost.",
  notes =        "
                 Workshop proceedings on CD-ROM only. Workshop held
                 in-conjunction with the IX Congress of the Italian
                 Association for Artificial Intelligence. In

                 ICAR-CNR, Via P.Bucci 41C, Univ. della Calabria 87036
                 Rende (CS), Italy

                 See \cite{Folino:2005:ieeeTEC}",

Genetic Programming entries for Gianluigi Folino Clara Pizzuti Giandomenico Spezzano