Generating Effective Classifiers with Supervised Learning of Genetic Programming

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

@InProceedings{Chien:2003:DaWaK,
  author =       "Been-Chian Chien and Jui-Hsiang Yang and 
                 Wen-Yang Lin",
  title =        "Generating Effective Classifiers with Supervised
                 Learning of Genetic Programming",
  booktitle =    "Data Warehousing and Knowledge Discovery: 5th
                 International Conference, DaWaK 2003",
  year =         "2003",
  volume =       "2737",
  series =       "Lecture Notes in Computer Science",
  pages =        "192--201",
  address =      "Prague, Czech Republic",
  month =        "3-5 " # sep,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1007/b11825",
  ISBN =         "3-540-40807-X",
  abstract =     "A new approach of learning classifiers using genetic
                 programming has been developed recently. Most of the
                 previous researches generate classification rules to
                 classify data. However, the generation of rules is time
                 consuming and the recognition accuracy is limited. In
                 this paper, an approach of learning classification
                 functions by genetic programming is proposed for
                 classification. Since a classification function deals
                 with numerical attributes only, the proposed scheme
                 first transforms the nominal data into numerical values
                 by rough membership functions. Then, the learning
                 technique of genetic programming is used to generate
                 classification functions. For the purpose of improving
                 the accuracy of classification, we proposed an adaptive
                 interval fitness function. Combining the learned
                 classification functions with training samples, an
                 effective classification method is presented. Numbers
                 of data sets selected from UCI Machine Learning
                 repository are used to show the effectiveness of the
                 proposed method and compare with other classifiers.",
}

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

Citations