Lymphoma Cancer Classification Using Genetic Programming with SNR Features

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{hong:2004:eurogp,
  author =       "Jin-Hyuk Hong and Sung Bae Cho",
  title =        "Lymphoma Cancer Classification Using Genetic
                 Programming with SNR Features",
  booktitle =    "Genetic Programming 7th European Conference, EuroGP
                 2004, Proceedings",
  year =         "2004",
  editor =       "Maarten Keijzer and Una-May O'Reilly and 
                 Simon M. Lucas and Ernesto Costa and Terence Soule",
  volume =       "3003",
  series =       "LNCS",
  pages =        "78--88",
  address =      "Coimbra, Portugal",
  publisher_address = "Berlin",
  month =        "5-7 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-21346-5",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=78",
  DOI =          "doi:10.1007/978-3-540-24650-3_8",
  abstract =     "Lymphoma cancer classification with DNA microarray
                 data is one of important problems in bioinformatics.
                 Many machine learning techniques have been applied to
                 the problem and produced valuable results. However the
                 medical field requires not only a high-accuracy
                 classifier, but also the in-depth analysis and
                 understanding of classification rules obtained. Since
                 gene expression data have thousands of features, it is
                 nearly impossible to represent and understand their
                 complex relationships directly. We adopt the SNR
                 (Signal-to-Noise Ratio) feature selection to reduce the
                 dimensionality of the data, and then use genetic
                 programming to generate cancer classification rules
                 with the features. In the experimental results on
                 Lymphoma cancer dataset, the proposed method yielded
                 96.6% test accuracy in average, and an excellent
                 arithmetic classification rule set that classifies all
                 the samples correctly is discovered by the proposed
                 method.",
  notes =        "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
                 conjunction with EvoCOP2004 and EvoWorkshops2004",
}

Genetic Programming entries for Jin-Hyuk Hong Sung Bae Cho

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