Features Selection based on Rough Membership and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Been-Chian Chien and Jui-Hsiang Yang",
  title =        "Features Selection based on Rough Membership and
                 Genetic Programming",
  booktitle =    "IEEE International Conference on Systems, Man and
                 Cybernetics, ICSMC '06",
  year =         "2006",
  volume =       "5",
  pages =        "4124--4129",
  address =      "Taipei, Taiwan",
  month =        "8-11 " # oct,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-4244-0100-3",
  DOI =          "doi:10.1109/ICSMC.2006.384780",
  abstract =     "This paper discusses the feature selection problem
                 upon supervised learning. A learning method based on
                 rough sets and genetic programming is proposed to
                 select significant features and classify numerical
                 data. The proposed method uses rough membership to
                 transform nominal data into numerical values, then
                 selects important features and learns classification
                 functions using genetic programming. We use several UCI
                 data sets to show the performance of the proposed
                 scheme and make comparisons with three different
                 features selection approaches: distance measure,
                 information measure and dependence measure. The results
                 demonstrate that the proposed method is effective both
                 in features selection and classification.",
  notes =        "Member, IEEE, National University of Tainan, Tainan
                 700, Taiwan, R. O. C. Tel: +886-6-2606123 ext. 7707,

Genetic Programming entries for Been-Chian Chien Jui-Hsiang Yang