Towards new directions of data mining by evolutionary fuzzy rules and symbolic regression

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@Article{Kromer:2013:CMA,
  author =       "P. Kromer and S. Owais and J. Platos and V. Snasel",
  title =        "Towards new directions of data mining by evolutionary
                 fuzzy rules and symbolic regression",
  journal =      "Computer and Mathematics with Applications",
  year =         "2013",
  volume =       "66",
  number =       "2",
  month =        aug,
  pages =        "190--200",
  keywords =     "genetic algorithms, genetic programming, Fuzzy rules,
                 Fuzzy information retrieval, Data mining, Application",
  ISSN =         "0898-1221",
  DOI =          "doi:10.1016/j.camwa.2013.02.017",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0898122113001284",
  size =         "11 pages",
  abstract =     "There are various techniques for data mining and data
                 analysis. Among them, hybrid approaches combining two
                 or more fundamental methods gain importance as the
                 complexity and dimension of real world problems and
                 data sets grows. Fuzzy sets and fuzzy logic can be used
                 for efficient data classification by the means of fuzzy
                 rules and classifiers. This study presents an
                 application of genetic programming to the evolution of
                 fuzzy rules based on the concept of extended Boolean
                 queries. Fuzzy rules are used as symbolic classifiers
                 learnt from data and used to label data records and to
                 predict the value of an output variable. An example of
                 the application of such a hybrid evolutionary-fuzzy
                 data mining approach to a real world problem is
                 presented.",
}

Genetic Programming entries for Pavel Kromer Suhail S J Owais Jan Platos Vaclav Snasel

Citations