Genetic Network Programming for Fuzzy Association Rule-Based Classification

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

@InProceedings{Taboada:2009:cec,
  author =       "Karla Taboada and Shingo Mabu and Eloy Gonzales and 
                 Kaoru Shimada and Kotaro Hirasawa",
  title =        "Genetic Network Programming for Fuzzy Association
                 Rule-Based Classification",
  booktitle =    "2009 IEEE Congress on Evolutionary Computation",
  year =         "2009",
  editor =       "Andy Tyrrell",
  pages =        "2387--2394",
  address =      "Trondheim, Norway",
  month =        "18-21 " # may,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-2959-2",
  file =         "P662.pdf",
  DOI =          "doi:10.1109/CEC.2009.4983239",
  abstract =     "This paper presents a novel classification approach
                 that integrates fuzzy classification rules and Genetic
                 Network Programming (GNP). A fuzzy discretization
                 technique is applied to transform the dataset,
                 particularly for dealing with quantitative attributes.
                 GNP is an evolutionary optimization technique that uses
                 directed graph structures as genes instead of strings
                 and trees of Genetic Algorithms (GA) and Genetic
                 Programming (GP), respectively. This feature
                 contributes to creating quite compact programs and
                 implicitly memorizing past action sequences. Therefore,
                 in the proposed method, taking the GNP's structure into
                 account 1) extraction of fuzzy classification rules is
                 done without identifying frequent itemsets used in most
                 Apriori-based data mining algorithms, 2) calculation of
                 the support, confidence and Χ2 value is
                 made in order to quantify the significance of the rules
                 to be integrated into the classifier, 3) fuzzy
                 membership values are used for fuzzy classification
                 rules extraction, 4) fuzzy rules are mined through
                 generations and stored in a general pool. On the other
                 hand, parameters of the membership functions are
                 evolved by non-uniform mutation in order to perform a
                 more global search in the space of candidate membership
                 functions. The performance of our algorithm has been
                 compared with other relevant algorithms and the
                 experimental results have shown the advantages and
                 effectiveness of the proposed model.",
  keywords =     "genetic algorithms, genetic programming, genetic
                 network programming",
  notes =        "CEC 2009 - A joint meeting of the IEEE, the EPS and
                 the IET. IEEE Catalog Number: CFP09ICE-CDR",
}

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

Citations