Improving GP classifier generalization using a cluster separation metric

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

@InProceedings{1144159,
  author =       "Ashley George and Malcolm I. Heywood",
  title =        "Improving GP classifier generalization using a cluster
                 separation metric",
  booktitle =    "{GECCO 2006:} Proceedings of the 8th annual conference
                 on Genetic and evolutionary computation",
  year =         "2006",
  editor =       "Maarten Keijzer and Mike Cattolico and Dirk Arnold and 
                 Vladan Babovic and Christian Blum and Peter Bosman and 
                 Martin V. Butz and Carlos {Coello Coello} and 
                 Dipankar Dasgupta and Sevan G. Ficici and James Foster and 
                 Arturo Hernandez-Aguirre and Greg Hornby and 
                 Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and 
                 Franz Rothlauf and Conor Ryan and Dirk Thierens",
  volume =       "1",
  ISBN =         "1-59593-186-4",
  pages =        "939--940",
  address =      "Seattle, Washington, USA",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p939.pdf",
  DOI =          "doi:10.1145/1143997.1144159",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, 10286-1405, USA",
  month =        "8-12 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming: Poster,
                 classification, clustering, evaluation",
  size =         "2 pages",
  abstract =     "Genetic Programming offers freedom in the definition
                 of the cost function that is unparalleled among
                 supervised learning algorithms. However, this freedom
                 goes largely unexploited in previous work. Here, we
                 revisit the design of fitness functions for genetic
                 programming by explicitly considering the contribution
                 of the wrapper and cost function. Within the context of
                 supervised learning, as applied to classification
                 problems, a clustering methodology is introduced using
                 cost functions which encourage maximization of
                 separation between in and out of class exemplars.
                 Through a series of empirical investigations of the
                 nature of these functions, we demonstrate that
                 classifier performance is much more dependable than
                 previously the case under the genetic programming
                 paradigm.",
  notes =        "GECCO-2006 A joint meeting of the fifteenth
                 international conference on genetic algorithms
                 (ICGA-2006) and the eleventh annual genetic programming
                 conference (GP-2006).

                 ACM Order Number 910060",
}

Genetic Programming entries for Ashley George Malcolm Heywood

Citations