Evolutionary Intelligence

, 2:73 | Cite as

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

Special Issue

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.

Keywords

Genetic fuzzy systems Vague data Fuzzy-knowledge-based rules Cooperative-competitive learning Possibilistic data 

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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Ana M. Palacios
    • 1
  • Luciano Sánchez
    • 1
  • Inés Couso
    • 2
  1. 1.Departamento de InformáticaUniversidad de OviedoGijónSpain
  2. 2.Departamento de Estadística e I.O. y D.MUniversidad de OviedoGijónSpain

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