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Extending a simple genetic cooperative-competitive learning fuzzy classifier to low quality datasets

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Abstract

Exploiting the information in low quality datasets has been recently acknowledged as a new challenge in Genetic Fuzzy Systems. Owing to this, in this paper we discuss the basic principles that govern the extension of a fuzzy rule based classifier to interval and fuzzy data. We have also applied these principles to the genetic learning of a simple cooperative-competitive algorithm, that becomes the first example of a Genetic Fuzzy Classifier able to use low quality data. Additionally, we introduce a benchmark, comprising some synthetic samples and two real-world problems that involve interval and fuzzy-valued data, that can be used to assess future algorithms of the same kind.

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Acknowledgments

This work was supported by the Spanish Ministry of Education and Science, under grants TIN2008-06681-C06-04, TIN2007-67418-C03-03, and by Principado de Asturias, under grant PCTI 2006-2009.

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Authors and Affiliations

  1. Departamento de Informática, Universidad de Oviedo, 33071, Gijón, Asturias, Spain

    Ana M. Palacios & Luciano Sánchez

  2. Departamento de Estadística e I.O. y D.M, Universidad de Oviedo, 33071, Gijón, Asturias, Spain

    Inés Couso

Authors
  1. Ana M. Palacios
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  2. Luciano Sánchez
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  3. Inés Couso
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Corresponding author

Correspondence to Luciano Sánchez.

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Palacios, A.M., Sánchez, L. & Couso, I. Extending a simple genetic cooperative-competitive learning fuzzy classifier to low quality datasets. Evol. Intel. 2, 73 (2009). https://doi.org/10.1007/s12065-009-0024-1

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  • DOI: https://doi.org/10.1007/s12065-009-0024-1

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