Applying correlation to enhance boosting technique using genetic programming as base learner

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  title =        "Applying correlation to enhance boosting technique
                 using genetic programming as base learner",
  author =       "Luzia Vidal {de Souza} and Aurora Pozo and 
                 Joel Mauricio Correa {da Rosa} and Anselmo Chaves Neto",
  journal =      "Applied Intelligence",
  year =         "2010",
  number =       "3",
  volume =       "33",
  pages =        "291--301",
  keywords =     "genetic algorithms, genetic programming",
  publisher =    "Springer Netherlands",
  ISSN =         "0924-669X",
  DOI =          "doi:10.1007/s10489-009-0166-y",
  size =         "11 pages",
  abstract =     "This paper explores the Genetic Programming and
                 Boosting technique to obtain an ensemble of regressors
                 and proposes a new formula for the updating of weights,
                 as well as for the final hypothesis. Differently from
                 studies found in the literature, in this paper we
                 investigate the use of the correlation metric as an
                 additional factor for the error metric. This new
                 approach, called Boosting using Correlation
                 Coefficients (BCC) has been empirically obtained after
                 trying to improve the results of the other methods. To
                 validate this method, we conducted two groups of
                 experiments. In the first group, we explore the BCC for
                 time series forecasting, in academic series and in a
                 widespread Monte Carlo simulation covering the entire
                 ARMA spectrum. The Genetic Programming (GP) is used as
                 a base learner and the mean squared error (MSE) has
                 been used to compare the accuracy of the proposed
                 method against the results obtained by GP, GP using
                 traditional boosting and the traditional statistical
                 methodology (ARMA). The second group of experiments
                 aims at evaluating the proposed method on multivariate
                 regression problems by choosing Cart (Classification
                 and Regression Tree) as the base learner.",
  affiliation =  "University of Parana (UFPR), CP 19:081, CEP: 81531-970
                 Curitiba, Brazil",

Genetic Programming entries for Luzia Vidal de Souza Aurora Trinidad Ramirez Pozo Joel Mauricio Correa da Rosa Anselmo Chaves Neto