Constructive Induction of Fuzzy Cartesian Granule Feature Models using Genetic Programming with Applications

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "James G. Shanahan and James F. Baldwin and 
                 Trevor P. Martin",
  title =        "Constructive Induction of Fuzzy Cartesian Granule
                 Feature Models using Genetic Programming with
  booktitle =    "Proceedings of the Congress on Evolutionary
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and 
                 Marc Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "1",
  pages =        "218--226",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, learning,
                 Cartesian granule fuzzy sets, G-DACG, Genetic Discovery
                 of Additive Cartesian Granule feature models , additive
                 Cartesian granule feature models, constituent input
                 features, constructive induction algorithm, exponential
                 search problem, fitness function, fuzzy Cartesian
                 granule feature models, linguistic partitioning,
                 optimisation capabilities, prediction problems, real
                 world classification problems, rule based models,
                 semantic separation, computational linguistics, fuzzy
                 set theory, learning by example, pattern
  DOI =          "doi:10.1109/CEC.1999.781929",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  abstract =     "Cartesian granule features are derived features that
                 are formed over the cross product of words that
                 linguistically partition the universes of the
                 constituent input features. Both classification and
                 prediction problems can be modelled quite naturally in
                 terms of Cartesian granule features incorporated into
                 rule based models. The induction of Cartesian granule
                 feature model involves discovering which input features
                 should be combined to form Cartesian granule features
                 in order to model a domain effectively; an exponential
                 search problem. We present the G-DACG (Genetic
                 Discovery of Additive Cartesian Granule feature models)
                 constructive induction algorithm as a means of
                 automatically identifying additive Cartesian granule
                 feature models from example data. G-DACG combines the
                 powerful optimisation capabilities of genetic
                 programming with a rather novel and cheap fitness
                 function which relies on the semantic separation of
                 learnt concepts expressed in terms of Cartesian granule
                 fuzzy sets. G-DACG is illustrated on a variety of
                 artificial and real world classification problems",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",

Genetic Programming entries for James G Shanahan James F Baldwin Trevor P Martin