Classification of colon tumor tissues using genetic programming

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

@InProceedings{Archetti:2008:wivace,
  author =       "Francesco Archetti and Mauro Castelli and 
                 Ilaria Giordani and Leonardo Vanneschi",
  title =        "Classification of colon tumor tissues using genetic
                 programming",
  booktitle =    "Artificial Life and Evolutionary Computation:
                 Proceedings of Wivace 2008",
  year =         "2008",
  editor =       "J. Roberto Serra and Marco Villani and Irene Poli",
  pages =        "49--58",
  address =      "Venice, Italy",
  month =        "8-10 " # sep,
  publisher =    "World Scientific Publishing Co.",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "9789814287456",
  URL =          "ftp://ftp.ce.unipr.it/pub/cagnoni/WIV08/paper%202.pdf",
  broken =       "http://ebooks.worldscinet.com/ISBN/9789814287456/9789814287456_0004.html",
  size =         "10 pages",
  abstract =     "A Genetic Programming (GP) framework for
                 classification is presented in this paper and applied
                 to a publicly available biomedical microarray dataset
                 representing a collection of expression measurements
                 from colon biopsy experiments [3]. We report
                 experimental results obtained using two different well
                 known fitness criteria: the area under the receiving
                 operating curve (ROC) and the percentage of correctly
                 classified instances (CCI). These results, and their
                 comparison with the ones obtained by three
                 non-evolutionary Machine Learning methods (Support
                 Vector Machines, Voted Perceptron and Random Forests)
                 on the same data, seem to hint that GP is a promising
                 technique for this kind of classification both from the
                 viewpoint of the accuracy of the proposed solutions and
                 of the generalisation ability. These results are
                 encouraging and should pave the way to a deeper study
                 of GP for classification applied to biomedical
                 microarray data sets.",
  notes =        "Workshop Italiano di Vita Artificiale e Computazione
                 Evolutiva http://wivace.unimore.it/

                 Dept. of Informatics, Systems and Communication,
                 University of Milano-Bicocca, 20126 Milan,
                 Italy

                 http://microarray.princeton.edu/oncology/affydata/index.html",
}

Genetic Programming entries for Francesco Archetti Mauro Castelli Ilaria Giordani Leonardo Vanneschi

Citations