Evolutionary Data Mining Approaches for Rule-based and Tree-based Classifiers

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Thomas Weise and Raymond Chiong",
  title =        "Evolutionary Data Mining Approaches for Rule-based and
                 Tree-based Classifiers",
  booktitle =    "9th IEEE International Conference on Cognitive
                 Informatics (ICCI 2010)",
  year =         "2010",
  editor =       "Fuchun Sun and Yingxu Wang and Jianhua Lu and 
                 Bo Zhang and Witold Kinsner and Lotfi A. Zadeh",
  pages =        "696--703",
  address =      "Tsinghua University, Beijing, China",
  month =        "7-9 " # jul,
  publisher =    "IEEE",
  note =         "Special Session on Evolutionary Computing",
  email =        "tweise@gmx.de",
  keywords =     "genetic algorithms, genetic programming, data mining,
                 decision trees, rule-based classifiers, C4.5 approach,
                 decision trees, evolutionary algorithms, evolutionary
                 data mining approach, random-forest approach, rule set
                 encoding, rule-based classifier, supervised data mining
                 approach, tree-based classifiers, data mining,
                 knowledge based systems, pattern classification",
  isbn13 =       "978-1-4244-8040-1",
  URL =          "http://www.it-weise.de/documents/files/WC2010EDMAFRBATBC.pdf",
  DOI =          "doi:10.1109/COGINF.2010.5599821",
  abstract =     "Data mining is an important process, with applications
                 found in many business, science and industrial
                 problems. While a wide variety of algorithms have
                 already been proposed in the literature for
                 classification tasks in large data sets, and the
                 majority of them have been proven to be very effective,
                 not all of them are flexible and easily extensible. In
                 this paper, we introduce two new approaches for
                 synthesising classifiers with Evolutionary Algorithms
                 (EAs) in supervised data mining scenarios. The first
                 method is based on encoding rule sets with bit string
                 genomes and the second one uses Genetic Programming to
                 create decision trees with arbitrary expressions
                 attached to the nodes. Comparisons with some
                 sophisticated standard approaches, such as C4.5 and
                 Random-Forest, show that the performance of the evolved
                 classifiers can be very competitive. We further
                 demonstrate that both proposed approaches work well
                 across different configurations of the EAs.",
  notes =        "http://www.icci2010.edu.cn/ Also known as

Genetic Programming entries for Thomas Weise Raymond Chiong