Representing Classification Problems in Genetic Programming

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

@InProceedings{LovCie01,
  author =       "Thomas Loveard and Victor Ciesielski",
  title =        "Representing Classification Problems in Genetic
                 Programming",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "2001",
  volume =       "2",
  pages =        "1070--1077",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  email =        "toml@cs.rmit.edu.au",
  keywords =     "genetic algorithms, genetic programming,
                 Classification",
  ISBN =         "0-7803-6657-3",
  URL =          "http://goanna.cs.rmit.edu.au/~toml/cec2001.ps",
  DOI =          "doi:10.1109/CEC.2001.934310",
  abstract =     "In this paper five alternative methods are proposed to
                 perform multi-class classification tasks using genetic
                 programming. These methods are: Binary decomposition,
                 in which the problem is decomposed into a set of binary
                 problems and standard genetic programming methods are
                 applied; Static range selection, where the set of real
                 values returned by a genetic program is divided into
                 class boundaries using arbitrarily chosen division
                 points; Dynamic range selection in which a subset of
                 training examples are used to determine where, over the
                 set of reals, class boundaries lie; Class enumeration
                 which constructs programs similar in syntactic
                 structure to a decision tree; and evidence accumulation
                 which allows separate branches of the program to add to
                 the certainty of any given class. Results showed that
                 the dynamic range selection method was well suited to
                 the task of multi-class classification and was capable
                 of producing classifiers more accurate than the other
                 methods tried when comparable training times were
                 allowed. Accuracy of the generated classifiers was
                 comparable to alternative approaches over several
                 datasets.",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number = .

                 Tested on UCI machine learning testsets. STGP. 5
                 approaches to multiclass classifications: binary
                 decomposition, static range, dynamic range, class
                 enumeration (additional data type {"}ClassType{"} (cf
                 C4.5), evidence accumulation cf {"}AddToClass{"}, cf
                 Teller",
}

Genetic Programming entries for Thomas Loveard Victor Ciesielski

Citations