Genetic Programming for Generating Prototypes in Classification Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "L. P. Cordella and C. {De Stefano} and 
                 F. Fontanella and A. Marcelli",
  title =        "Genetic Programming for Generating Prototypes in
                 Classification Problems",
  booktitle =    "Proceedings of the 2005 IEEE Congress on Evolutionary
  year =         "2005",
  editor =       "David Corne and Zbigniew Michalewicz and 
                 Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and 
                 Garrison Greenwood and Tan Kay Chen and 
                 Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and 
                 Jennifier Willies and Juan J. Merelo Guervos and 
                 Eugene Eberbach and Bob McKay and Alastair Channon and 
                 Ashutosh Tiwari and L. Gwenn Volkert and 
                 Dan Ashlock and Marc Schoenauer",
  volume =       "2",
  pages =        "1149--1155",
  address =      "Edinburgh, UK",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "2-5 " # sep,
  organisation = "IEEE Computational Intelligence Society, Institution
                 of Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7803-9363-5",
  DOI =          "doi:10.1109/CEC.2005.1554820",
  abstract =     "We propose a genetic programming based approach for
                 generating prototypes in a classification problem. In
                 this context, the set of prototypes to which the
                 samples of a data set can be traced back is coded by a
                 multitree, i.e. a set of trees, which represents the
                 chromosome. Differently from other approaches, our
                 chromosomes are of variable length. This allows coping
                 with those classification problems in which one or more
                 classes consist of subclasses. The devised approach has
                 been tested on several problems and the results
                 compared with those obtained by a different genetic
                 programming based approach recently proposed in the
  notes =        "First author is not L. P. Cordelia. CEC2005 - A joint
                 meeting of the IEEE, the IEE, and the EPS.",

Genetic Programming entries for Luigi Pietro Cordella Claudio De Stefano Francesco R Fontanella Angelo Marcelli