Multiclass Object Classification Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Mengjie Zhang and Will Smart",
  title =        "Multiclass Object Classification Using Genetic
  booktitle =    "Applications of Evolutionary Computing,
                 EvoWorkshops2004: {EvoBIO}, {EvoCOMNET}, {EvoHOT},
                 {EvoIASP}, {EvoMUSART}, {EvoSTOC}",
  year =         "2004",
  month =        "5-7 " # apr,
  editor =       "Guenther R. Raidl and Stefano Cagnoni and 
                 Jurgen Branke and David W. Corne and Rolf Drechsler and 
                 Yaochu Jin and Colin R. Johnson and Penousal Machado and 
                 Elena Marchiori and Franz Rothlauf and George D. Smith and 
                 Giovanni Squillero",
  series =       "LNCS",
  volume =       "3005",
  address =      "Coimbra, Portugal",
  publisher =    "Springer Verlag",
  publisher_address = "Berlin",
  pages =        "369--378",
  keywords =     "genetic algorithms, genetic programming, evolutionary
  ISBN =         "3-540-21378-3",
  DOI =          "doi:10.1007/978-3-540-24653-4_38",
  abstract =     "We describe an approach to the use of genetic
                 programming for multiclass object classification
                 problems. Rather than using fixed static thresholds as
                 boundaries to distinguish between different classes,
                 this approach introduces two methods of classification
                 where the boundaries between different classes can be
                 dynamically determined during the evolutionary process.
                 The two methods are centred dynamic class boundary
                 determination and slotted dynamic class boundary
                 determination. The two methods are tested on four
                 object classification problems of increasing difficulty
                 and are compared with the commonly used static class
                 boundary determination method. The results suggest
                 that, while the static class boundary determination
                 method works well on relatively easy object
                 classification problems, the two dynamic class boundary
                 determination methods outperform the static method for
                 more difficult multiple class object classification
  notes =        "EvoWorkshops2004",

Genetic Programming entries for Mengjie Zhang Will Smart