Probability Based Genetic Programming for Multiclass Object Classification

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

@InProceedings{conf/pricai/SmartZ04,
  title =        "Probability Based Genetic Programming for Multiclass
                 Object Classification",
  author =       "Will Smart and Mengjie Zhang",
  booktitle =    "{PRICAI} 2004: Trends in Artificial Intelligence, 8th
                 Pacific Rim International Conference on Artificial
                 Intelligence",
  publisher =    "Springer",
  year =         "2004",
  volume =       "3157",
  editor =       "Chengqi Zhang and Hans W. Guesgen and Wai-Kiang Yeap",
  pages =        "251--261",
  series =       "Lecture Notes in Computer Science",
  address =      "Auckland, New Zealand",
  month =        aug # " 9-13",
  bibdate =      "2004-09-23",
  bibsource =    "DBLP",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-22817-9",
  DOI =          "doi:10.1007/b99563",
  size =         "11 pages",
  abstract =     "This paper describes a probability based genetic
                 programming (GP) approach to multiclass object
                 classification problems. Instead of using predefined
                 multiple thresholds to form different regions in the
                 program output space for different classes, this
                 approach uses probabilities of different classes,
                 derived from Gaussian distributions, to construct the
                 fitness function for classification. Two fitness
                 measures, overlap area and weighted distribution
                 distance, have been developed. The approach is examined
                 on three multiclass object classification problems of
                 increasing difficulty and compared with a basic GP
                 approach. The results suggest that the new approach is
                 more effective and more efficient than the basic GP
                 approach. While the area measure was a bit more
                 effective than the distance measure in most cases, the
                 distance measure was more efficient to learn good
                 program classifiers.",
  notes =        "Fri, 02 Jun 2006 17:03:20 +0800",
}

Genetic Programming entries for Will Smart Mengjie Zhang

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