Training Distributed GP Ensemble With a Selective Algorithm Based on Clustering and Pruning for Pattern Classification

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

  author =       "Gianluigi Folino and Clara Pizzuti and 
                 Giandomenico Spezzano",
  title =        "Training Distributed GP Ensemble With a Selective
                 Algorithm Based on Clustering and Pruning for Pattern
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2008",
  month =        aug,
  volume =       "12",
  number =       "4",
  pages =        "458--468",
  keywords =     "genetic algorithms, genetic programming, boosting
                 algorithm, cellular genetic programming, decision
                 trees, distributed hybrid environment, fittest trees,
                 pattern classification, pruning strategies, training
                 distributed GP ensemble, decision trees, pattern
  DOI =          "doi:10.1109/TEVC.2007.906658",
  ISSN =         "1089-778X",
  abstract =     "A boosting algorithm based on cellular genetic
                 programming (GP) to build an ensemble of predictors is
                 proposed. The method evolves a population of trees for
                 a fixed number of rounds and, after each round, it
                 chooses the predictors to include in the ensemble by
                 applying a clustering algorithm to the population of
                 classifiers. Clustering the population allows the
                 selection of the most diverse and fittest trees that
                 best contribute to improve classification accuracy. The
                 method proposed runs on a distributed hybrid
                 environment that combines the island and cellular
                 models of parallel GP. The combination of the two
                 models provides an efficient implementation of
                 distributed GP, and, at the same time, the generation
                 of low sized and accurate decision trees. The large
                 amount of memory required to store the ensemble affects
                 the performance of the method. This paper shows that,
                 by applying suitable pruning strategies, it is possible
                 to select a subset of the classifiers without
                 increasing misclassification errors; indeed for some
                 data sets, for up to 30percent of pruning, ensemble
                 accuracy increases. Experimental results show that the
                 combination of clustering and pruning enhances
                 classification accuracy of the ensemble approach.",
  notes =        "Also known as \cite{4439200}",

Genetic Programming entries for Gianluigi Folino Clara Pizzuti Giandomenico Spezzano