Feature extraction using multi-objective genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Yang Zhang and Peter I. Rockett",
  title =        "Feature extraction using multi-objective genetic
  booktitle =    "Multi-Objective Machine Learning",
  publisher =    "Springer",
  year =         "2006",
  editor =       "Yaochu Jin",
  volume =       "16",
  series =       "Studies in Computational Intelligence",
  chapter =      "4",
  pages =        "75--99",
  note =         "Invited chapter",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 Multi-objective Optimisation, Multi-objective Machine
  ISBN =         "3-540-30676-5",
  DOI =          "doi:10.1007/3-540-33019-4_4",
  abstract =     "A generic, optimal feature extraction method using
                 multi-objective genetic programming (MOGP) is
                 presented. This methodology has been applied to the
                 well-known edge detection problem in image processing
                 and detailed comparisons made with the Canny edge
                 detector. We show that the superior performance from
                 MOGP in terms of minimising the misclassification is
                 due to its effective optimal feature extraction.
                 Furthermore, to compare different evolutionary
                 approaches, two popular techniques - PCGA and SPGA -
                 have been extended to genetic programming as PCGP and
                 SPGP, and applied to five datasets from the UCI
                 database. Both of these evolutionary approaches provide
                 comparable misclassification errors within the present
                 framework but PCGP produces more compact
  notes =        "http://www.springer.com/sgw/cda/frontpage/0,11855,4-175-22-106797000-detailsPage%253Dppmmedia%257CaboutThisBook%257CaboutThisBook,00.html


Genetic Programming entries for Yang Zhang Peter I Rockett