Evolutionary Constructive Induction

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Mohammed Muharram and George D. Smith",
  title =        "Evolutionary Constructive Induction",
  journal =      "IEEE Transactions on Knowledge and Data Engineering",
  volume =       "17",
  number =       "11",
  year =         "2005",
  pages =        "1518--1528",
  publisher =    "IEEE Computer Society",
  address =      "Los Alamitos, CA, USA",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming, Feature
                 construction, classification",
  ISSN =         "1041-4347",
  DOI =          "doi:10.1109/TKDE.2005.182",
  abstract =     "Feature construction in classification is a
                 preprocessing step in which one or more new attributes
                 are constructed from the original attribute set, the
                 object being to construct features that are more
                 predictive than the original feature set. Genetic
                 programming allows the construction of nonlinear
                 combinations of the original features. We present a
                 comprehensive analysis of genetic programming (GP) used
                 for feature construction, in which four different
                 fitness functions are used by the GP and four different
                 classification techniques are subsequently used to
                 build the classifier. Comparisons are made of the error
                 rates and the size and complexity of the resulting
                 trees. We also compare the overall performance of GP in
                 feature construction with that of GP used directly to
                 evolve a decision tree classifier, with the former
                 proving to be a more effective use of the evolutionary
  notes =        "


Genetic Programming entries for Mohammed A Muharram George D Smith