Using Genetic Programming for Multiclass Classification by Simultaneously Solving Component Binary Classification Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{eurogp:SmartZ05,
  author =       "William Smart and Mengjie Zhang",
  editor =       "Maarten Keijzer and Andrea Tettamanzi and 
                 Pierre Collet and Jano I. {van Hemert} and Marco Tomassini",
  title =        "Using Genetic Programming for Multiclass
                 Classification by Simultaneously Solving Component
                 Binary Classification Problems",
  booktitle =    "Proceedings of the 8th European Conference on Genetic
                 Programming",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3447",
  year =         "2005",
  address =      "Lausanne, Switzerland",
  month =        "30 " # mar # " - 1 " # apr,
  organisation = "EvoNet",
  keywords =     "genetic algorithms, genetic programming, image
                 classification",
  ISBN =         "3-540-25436-6",
  pages =        "227--239",
  DOI =          "doi:10.1007/b107383",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  abstract =     "In this paper a method is presented to solve a series
                 of multiple-class object classification problems using
                 Genetic Programming (GP). All component two-class
                 subproblems of the multiple-class problem are solved in
                 a single run, using a multi-objective fitness function.
                 Probabilistic methods are used, with each evolved
                 program required to solve only one subproblem. Programs
                 gain a fitness related to their rank at the subproblem
                 that they solve best. The new method is compared by
                 experiment to two other GP based methods on four
                 multiple-class classification problems of varying
                 difficulty. The new method outperforms the other
                 methods significantly in almost all experiments. The
                 new method often takes a longer running time, but
                 usually reaches a peak in accuracy very early.",
  notes =        "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
                 conjunction with EvoCOP2005 and EvoWorkshops2005.

                 PCM, PM, CBD. Shapes. New Zealand coins. Pictures of 4
                 people.",
}

Genetic Programming entries for Will Smart Mengjie Zhang

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