Discovering fuzzy classification rules using Genetic Network Programming

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@InProceedings{Taboada:2008:SICE,
  author =       "Karla Taboada and Eloy Gonzales and Kaoru Shimada and 
                 Shingo Mabu and Kotaro Hirasawa",
  title =        "Discovering fuzzy classification rules using Genetic
                 Network Programming",
  booktitle =    "SICE Annual Conference",
  year =         "2008",
  month =        "20-22 " # aug,
  pages =        "1788--1793",
  address =      "Japan",
  keywords =     "genetic algorithms, genetic programming, association
                 rule mining, classification rule mining, data mining,
                 directed graph, evolutionary optimization, fuzzy
                 classification rule, fuzzy set theory, genetic network
                 programming, data mining, directed graphs, fuzzy set
                 theory, pattern classification",
  DOI =          "doi:10.1109/SICE.2008.4654954",
  abstract =     "Classification rule mining is an active data mining
                 research area. Most related studies have shown how
                 binary valued datasets are handled. However, datasets
                 in real-world applications, usually consist of fuzzy
                 and quantitative values. As a result, the idea to
                 combine the different approaches with fuzzy set theory
                 has been applied more frequently in recent years. Fuzzy
                 sets can help to overcome the so-called sharp boundary
                 problem by allowing partial memberships to the
                 different sets, not only 1 and 0. On the other hand,
                 fuzzy sets theory has been shown to be a very useful
                 tool because the mined rules are expressed in
                 linguistic terms, which are more natural and
                 understandable for human beings. This paper proposes
                 the combination of fuzzy set theory and 'genetic
                 network programming' (GNP) for discovering fuzzy
                 classification rules from given quantitative data. GNP,
                 as an extension of genetic algorithms (GA) and genetic
                 programming (GP), is an evolutionary optimization
                 technique that uses directed graph structures as genes
                 instead of strings and trees; this feature contributes
                 creating quite compact programs and implicitly
                 memorizing past action sequences. At last, experimental
                 results conducted on a real world database verify the
                 performance of the proposed method.",
  notes =        "Also known as \cite{4654954}",
}

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

Citations