Classification Strategies for Image Classification in Genetic Programming

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

@InProceedings{SmartZhang:03:ivcnz,
  author =       "Will R. Smart and Mengjie Zhang",
  title =        "Classification Strategies for Image Classification in
                 Genetic Programming",
  booktitle =    "Proceeding of Image and Vision Computing NZ
                 International Conference",
  year =         "2003",
  editor =       "Donald Bailey",
  pages =        "402--407",
  month =        nov,
  publisher =    "Massey University",
  address =      "Palmerston North, New Zealand",
  keywords =     "genetic algorithms, genetic programming, object
                 recognition, centred dynamic range selection, slotted
                 dynamic range selection, genetic programs",
  URL =          "http://www.mcs.vuw.ac.nz/~mengjie/papers/will-meng-ivcnz03.pdf",
  size =         "6 pages",
  abstract =     "genetic programming for multi-class image recognition
                 problems. In this approach, the terminal set is
                 constructed with image pixel statistics, the function
                 set consists of arithmetic and conditional operators,
                 and the fitness function is based on classification
                 accuracy in the training set. Rather than using fixed
                 static thresholds as boundaries to distinguish between
                 different classes, this approach introduces two dynamic
                 methods of classification, namely centred dynamic range
                 selection and slotted dynamic range selection, based on
                 the returned value of an evolved genetic program where
                 the boundaries between different classes can be
                 dynamically determined during the evolutionary process.
                 The two dynamic methods are applied to five image
                 datasets of classification problems of increasing
                 difficulty and are compared with the commonly used
                 static range selection method. The results suggest
                 that, while the static boundary selection method works
                 well on relatively easy binary or tertiary image
                 classification problems with class labels arranged in
                 the natural order, the two dynamic range selection
                 methods outperform the static method for more
                 difficult, multiple class problems.",
  notes =        "Fri, 02 Jun 2006 17:03:20 +0800

                 ISBN 0-476-00095-5 (Paper version), ISBN 0-476-00096-3
                 (CD version)",
}

Genetic Programming entries for Will Smart Mengjie Zhang

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