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  • There are various techniques for data mining and data analysis. Among them, hybrid approaches combining two or more algorithms gain importance as the complexity and dimension of real world data sets grows. In this paper, we present an application of evolutionary-fuzzy classification technique for data mining. Genetic programming is deployed to evolve a fuzzy classifier and an example of real world application is presented.
  • There are various techniques for data mining and data analysis. Among them, hybrid approaches combining two or more algorithms gain importance as the complexity and dimension of real world data sets grows. In this paper, we present an application of evolutionary-fuzzy classification technique for data mining. Genetic programming is deployed to evolve a fuzzy classifier and an example of real world application is presented. (en)
Title
  • Data mining by symbolic fuzzy classifiers and genetic programming
  • Data mining by symbolic fuzzy classifiers and genetic programming (en)
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  • Data mining by symbolic fuzzy classifiers and genetic programming
  • Data mining by symbolic fuzzy classifiers and genetic programming (en)
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  • RIV/61989100:27740/13:86088872!RIV14-MSM-27740___
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  • P(ED1.1.00/02.0070), P(EE.2.3.20.0073), S
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  • 67952
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  • RIV/61989100:27740/13:86088872
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  • TEXT RETRIEVAL (en)
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  • [D7CB260554E4]
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  • Ostrava
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  • Berlin
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  • Advances in Intelligent Systems and Computing. Volume 210
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  • Krömer, Pavel
  • Platoš, Jan
  • Snášel, Václav
  • Zelinka, Ivan
  • Owais, Suhail
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issn
  • 2194-5357
number of pages
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  • 10.1007/978-3-642-33227-2_28
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  • Springer-Verlag
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  • 978-3-319-00541-6
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  • 27740
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