Fuzzy classification rule mining based on Genetic Network Programming algorithm

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

@InProceedings{Taboada:2009:ieeeSMC,
  author =       "Karla Taboada and Shingo Mabu and Eloy Gonzales and 
                 Kaoru Shimada and Kotaro Hirasawa",
  title =        "Fuzzy classification rule mining based on Genetic
                 Network Programming algorithm",
  booktitle =    "IEEE International Conference on Systems, Man and
                 Cybernetics, SMC 2009",
  year =         "2009",
  month =        oct,
  pages =        "3860--3865",
  keywords =     "genetic algorithms, genetic programming, association
                 rule mining, association rule-based classification,
                 data mining techniques, directed graph structures,
                 evolutionary optimization algorithms, fuzzy
                 classification rule mining, genetic network programming
                 algorithm, data mining, directed graphs, fuzzy set
                 theory",
  DOI =          "doi:10.1109/ICSMC.2009.5346640",
  ISSN =         "1062-922X",
  abstract =     "Association rule-based classification is one of the
                 most important data mining techniques applied to many
                 scientific problems. In the last few years, extensive
                 research has been carried out to develop enhanced
                 methods and obtained higher classification accuracies
                 than traditional classifiers. However, the current
                 studies show that the association rule-based
                 classifiers may also suffer some problems inherited
                 from association rule mining such as handling of (1)
                 continuous data and (2) the support/confidence
                 framework. In this paper, a novel fuzzy classification
                 model based on genetic network programming (GNP) that
                 can deal with the above problems has been proposed. GNP
                 is one of the evolutionary optimization algorithms that
                 uses directed graph structures as solutions instead of
                 strings (genetic algorithms) or trees (genetic
                 programming). Therefore, GNP can deal with more complex
                 problems by using the higher expression ability of
                 graph structures. The performance of our algorithm has
                 been compared with other relevant algorithms and the
                 experimental results show the advantages and
                 effectiveness of the proposed model.",
  notes =        "Also known as \cite{5346640}",
}

Genetic Programming entries for Karla Taboada Shingo Mabu Eloy Gonzales Kaoru Shimada Kotaro Hirasawa

Citations