Comparison between Genetic Programming and full model selection on classification problems

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Valencia-Ramirez:2014:ROPEC,
  author =       "J. M. Valencia-Ramirez and J. A. Raya and 
                 J. R. Cedeno and R. R. Suarez and H. J. Escalante and M. Graff",
  booktitle =    "IEEE International Autumn Meeting on Power,
                 Electronics and Computing (ROPEC 2014)",
  title =        "Comparison between Genetic Programming and full model
                 selection on classification problems",
  year =         "2014",
  month =        nov,
  abstract =     "Genetic Programming (GP) has been shown to be a
                 competitive classification technique. GP is generally
                 enhanced with a novel crossover, mutation, or selection
                 mechanism, in order to compare the performance of this
                 improvement with the performance of a standard GP.
                 Although these comparisons show the capabilities of GP,
                 it also makes harder, for a new comer, to figure out
                 whether a traditional GP would have a competitive
                 classification performance, when compared to
                 state-of-the-art techniques. In this work, we try to
                 fill this gap by comparing a standard GP, a GP with
                 minor modifications and a ensemble of GP with two
                 competitive techniques, namely support vector machines
                 and a procedure that performs full model selection
                 (Particle Swarm Model Selection). The results show that
                 GP has better performance on problems with high
                 dimensionality and large training sets and it is
                 competitive on the rest of the problems tested. The
                 former result is interesting because while Particle
                 Swarm Model Selection is tailored to perform a data
                 preprocessing and feature selection, GP is
                 automatically performing these tasks and producing
                 better classifiers.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/ROPEC.2014.7036349",
  notes =        "Also known as \cite{7036349}",
}

Genetic Programming entries for Jose Maria Valencia-Ramirez J A Raya J R Cedeno Ranyart Rodrigo Suarez Hugo Jair Escalante Mario Graff Guerrero

Citations