Breast cancer diagnosis using genetic programming generated feature

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@Article{GN:PR:06,
  title =        "Breast cancer diagnosis using genetic programming
                 generated feature",
  author =       "Hong Guo and Asoke K. Nandi",
  journal =      "Pattern Recognition",
  year =         "2006",
  volume =       "39",
  number =       "5",
  pages =        "980--987",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, Feature
                 extraction, Fisher discriminant analysis, Pattern
                 recognition",
  DOI =          "doi:10.1016/j.patcog.2005.10.001",
  size =         "8 pages",
  abstract =     "This paper proposes a novel method for breast cancer
                 diagnosis using the feature generated by genetic
                 programming (GP). We developed a new feature extraction
                 measure (modified Fisher linear discriminant analysis
                 (MFLDA)) to overcome the limitation of Fisher
                 criterion. GP as an evolutionary mechanism provides a
                 training structure to generate features. A modified
                 Fisher criterion is developed to help GP optimise
                 features that allow pattern vectors belonging to
                 different categories to distribute compactly and
                 disjoint regions. First, the MFLDA is experimentally
                 compared with some classical feature extraction methods
                 (principal component analysis, Fisher linear
                 discriminant analysis, alternative Fisher linear
                 discriminant analysis). Second, the feature generated
                 by GP based on the modified Fisher criterion is
                 compared with the features generated by GP using Fisher
                 criterion and an alternative Fisher criterion in terms
                 of the classification performance. The classification
                 is carried out by a simple classifier (minimum distance
                 classifier). Finally, the same feature generated by GP
                 is compared with a original feature set as the inputs
                 to multi-layer perceptrons and support vector machine.
                 Results demonstrate the capability of this method to
                 transform information from high-dimensional feature
                 space into one-dimensional space and automatically
                 discover the relationship among data, to improve
                 classification accuracy.",
}

Genetic Programming entries for Hong Guo Asoke K Nandi

Citations