Derivation of Relational Fuzzy Classification Rules Using Evolutionary Computation

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@InProceedings{Akbarzadeh:2008:fuzz,
  author =       "Vahab Akbarzadeh and Alireza Sadeghian and 
                 Marcus V. {dos Santos}",
  title =        "Derivation of Relational Fuzzy Classification Rules
                 Using Evolutionary Computation",
  booktitle =    "2008 IEEE World Congress on Computational
                 Intelligence",
  year =         "2008",
  editor =       "Jun Wang",
  pages =        "1689--1693",
  address =      "Hong Kong",
  month =        "1-6 " # jun,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-1819-0",
  file =         "FS0398.pdf",
  DOI =          "doi:10.1109/FUZZY.2008.4630598",
  ISSN =         "1098-7584",
  keywords =     "genetic algorithms, genetic programming,
                 constrained-syntax genetic programming, evolutionary
                 computation, knowledge-based systems, mutation-based
                 evolutionary algorithm, relational fuzzy classification
                 rules, fuzzy set theory, knowledge based systems",
  abstract =     "An evolutionary system for derivation of fuzzy
                 classification rules is presented. This system uses two
                 populations: one of fuzzy classification rules, and one
                 of membership function definitions. A
                 constrained-syntax genetic programming evolves the
                 first population and a mutation-based evolutionary
                 algorithm evolves the second population. These two
                 populations co-evolve to better classify the underlying
                 dataset. Unlike other approaches that use fuzzification
                 of continuous attributes of the dataset for discovering
                 fuzzy classification rules, the system presented here
                 fuzzifies the relational operators ``greater than'' and
                 ``less than'' using evolutionary methods. For testing
                 our system, the system is applied to the Iris dataset.
                 Our experimental results show that our system
                 outperforms previous evolutionary and non-evolutionary
                 systems on accuracy of classification and derivation of
                 interrelation between the attributes of the Iris
                 dataset. The resulting fuzzy rules of the system can be
                 directly used in knowledge-based systems.",
  notes =        "Also known as \cite{4630598} WCCI 2008 - A joint
                 meeting of the IEEE, the INNS, the EPS and the IET.",
}

Genetic Programming entries for Vahab Akbarzadeh Alireza Sadeghian Marcus Vinicius dos Santos

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