A Feature Transformation Method using Genetic Programming for Two-Class Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Tomoyuki Hiroyasu and Toshihide Shiraishi and 
                 Tomoya Yoshida and Utako Yamamoto",
  booktitle =    "IEEE Symposium on Computational Intelligence and Data
                 Mining (CIDM 2014)",
  title =        "A Feature Transformation Method using Genetic
                 Programming for Two-Class Classification",
  year =         "2014",
  month =        dec,
  pages =        "234--240",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CIDM.2014.7008673",
  size =         "7 pages",
  abstract =     "In this paper, a feature transformation method for
                 two-class classification using genetic programming (GP)
                 is proposed. GP derives a transformation formula to
                 improve the classification accuracy of Support Vector
                 Machine, SVM. In this paper, we propose a weight
                 function to evaluate converted feature space and the
                 proposed function is used to evaluate the function of
                 GP. In the proposed function, the ideal two-class
                 distribution of items is assumed and the distance
                 between the actual and ideal distributions is
                 calculated. The weight is imposed to these distances.
                 To examine the effectiveness of the proposed function,
                 a numerical experiment was performed. In the
                 experiment, as the result, the classification accuracy
                 of the proposed method showed the better result than
                 that of the existing method.",
  notes =        "Fac. of Life & Med. Sci., Doshisha Univ., Kyotanabe,

                 Also known as \cite{7008673}",

Genetic Programming entries for Tomoyuki Hiroyasu Toshihide Shiraishi Tomoya Yoshida Utako Yamamoto