Evolving model trees for mining data sets with continuous-valued classes

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Gavin Potgieter and Andries P. Engelbrecht",
  title =        "Evolving model trees for mining data sets with
                 continuous-valued classes",
  journal =      "Expert Systems with Applications",
  volume =       "35",
  number =       "4",
  pages =        "1513--1532",
  year =         "2008",
  keywords =     "genetic algorithms, genetic programming, Data mining,
                 Continuous-valued classes, Model trees",
  ISSN =         "0957-4174",
  URL =          "http://www.sciencedirect.com/science/article/B6V03-4PMT2TF-1/2/2951ff5c090ff34723645688b51c34cd",
  DOI =          "doi:10.1016/j.eswa.2007.08.060",
  abstract =     "This paper presents a genetic programming (GP)
                 approach to extract symbolic rules from data sets with
                 continuous-valued classes, called GPMCC. The GPMCC
                 makes use of a genetic algorithm (GA) to evolve
                 multi-variate non-linear models [Potgieter, G., &
                 Engelbrecht, A. (2007). Genetic algorithms for the
                 structural optimisation of learned polynomial
                 expressions. Applied Mathematics and Computation] at
                 the terminal nodes of the GP. Several mechanisms have
                 been developed to optimise the GP, including a fragment
                 pool of candidate non-linear models, k-means clustering
                 of the training data to facilitate the use of
                 stratified sampling methods, and specialized mutation
                 and crossover operators to evolve structurally optimal
                 and accurate models. It is shown that the GPMCC is
                 insensitive to control parameter values. Experimental
                 results show that the accuracy of the GPMCC is
                 comparable to that of NeuroLinear and Cubist, while
                 producing significantly less rules with less complex

Genetic Programming entries for Gavin Potgieter Andries P Engelbrecht