A Function-Based Classifier Learning Scheme Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Lin:2002:FBC,
  author =       "Jung-Yi Lin and Been-Chian Chien and Tzung-Pei Hong",
  title =        "A Function-Based Classifier Learning Scheme Using
                 Genetic Programming",
  booktitle =    "Advances in Knowledge Discovery and Data Mining : 6th
                 Pacific-Asia Conference, PAKDD 2002",
  editor =       "M.-S. Chen and P. S. Yu and B. Liu",
  year =         "2002",
  volume =       "2336",
  pages =        "92--103",
  series =       "Lecture Notes in Computer Science",
  address =      "Taipel, Taiwan",
  publisher_address = "Heidelberg",
  month =        "6-8 " # may,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-540-43704-8",
  CODEN =        "LNCSD9",
  ISSN =         "0302-9743",
  bibdate =      "Tue Sep 10 19:09:39 MDT 2002",
  DOI =          "doi:10.1007/3-540-47887-6_9",
  acknowledgement = ack-nhfb,
  size =         "12 pages",
  abstract =     "Classification is an important research topic in
                 knowledge discovery and data mining. Many different
                 classifiers have been motivated and developed of late
                 years. In this paper, we propose an effective scheme
                 for learning multi-category classifiers based on
                 genetic programming. For a $k$-class classification
                 problem, a training strategy called adaptive
                 incremental learning strategy and a new fitness
                 function are used to generate $k$ discriminant
                 functions. We urge the discriminant functions to map
                 the domains of training data into a specified interval,
                 and thus data will be assigned into one of the classes
                 by the values of functions. Furthermore, a $Z$-value
                 measure is developed for resolving the conflicts. The
                 experimental results show that the proposed GP-based
                 classification learning approach is effective and
                 performs a high accuracy of classification.",
  notes =        "http://arbor.ee.ntu.edu.tw/pakdd02/

                 chinese version
                 http://www.bohr.idv.tw/chinese/pdf/B013.pdf",
}

Genetic Programming entries for Mick Jung-Yi Lin Been-Chian Chien Tzung-Pei Hong

Citations