Evolutionary Intelligence

, 2:73 | Cite as

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

  • Ana M. Palacios
  • Luciano Sánchez
  • Inés Couso
Special Issue


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.


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



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.


  1. 1.
    Ajuriaguerra J (1976) Manual de psiquiatrí a infantil (in Spanish). Toray-Masson, BarcelonaGoogle Scholar
  2. 2.
    Cordón O, Herrera F, Hoffmann F, Magdalena L (2001) Genetic fuzzy systems Evolutionary tuning and learning of fuzzy knowledge bases. World Scientific, SingaporezbMATHGoogle Scholar
  3. 3.
    Couso I, Sánchez L (2008) Higher order models for fuzzy random variables. Fuzzy Sets Syst 159:237–258zbMATHCrossRefGoogle Scholar
  4. 4.
    Dubois D, Prade H (1987) The mean value of a fuzzy number. Fuzzy Sets Syst 24(3):279–300zbMATHCrossRefGoogle Scholar
  5. 5.
    Georgopulos V (2003) A fuzzy cognitive map to differential diagnosis of specific language impairment. Artif Intell Med 29:261–278CrossRefGoogle Scholar
  6. 6.
    Herrera F (2008) Genetic fuzzy systems: taxonomy, current research trends and prospects. Evol Intell 1:27–46CrossRefGoogle Scholar
  7. 7.
    Ishibuchi H, Nakashima T, Murata T (1995) A fuzzy classifier system that generates fuzzy if-then rules for pattern classification problems. In: Proceedings of 2nd IEEE international conference on evolutionary computation, pp 759–764Google Scholar
  8. 8.
    Koeppen M, Franke K, Nickolay B (2003) Fuzzy-pareto-dominance driven multi-objective genetic algorithm. In: Proceedings of 10th international fuzzy systems assotiation world congress (IFSA), Istanbul, Turkey, pp 450–453Google Scholar
  9. 9.
    Lakov DV (2004) Soft computing agent approach to remote learning of disables. In: 2nd IEEE international conference on intelligent systems, pp 250–255Google Scholar
  10. 10.
    Limbourg P (2005) Multi-objective optimization of problems with epistemic uncertainty. In: EMO 2005, pp 413–427Google Scholar
  11. 11.
    Medasani S, Kim J, Krishnapuram S (1998) An overview of membership function generation techniques for pattern recognition. Int J Approx Reason 19(3–4):391–417zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Sánchez L, Otero J, Villar JR (2006) Boosting of fuzzy models for high-dimensional imprecise datasets. In: Proceedings of IPMU 2006, Paris, pp 1965–1973Google Scholar
  13. 13.
    Sánchez L, Couso I (2007) Advocating the use of imprecisely observed data in genetic fuzzy systems. IEEE Trans Fuzzy Syst 15(4):551–562CrossRefGoogle Scholar
  14. 14.
    Sánchez L, Couso I, Casillas J (2007) Modelling vague data with genetic fuzzy systems under a combination of crisp and imprecise criteria. In: Proceedings of 2007 IEEE symposium on computational intelligence in multicriteria decision making, Honolulu, pp 30–37Google Scholar
  15. 15.
    Sánchez L, Palacios A, Couso I (2008) A minimum risk wrapper algorithm for genetically selecting imprecisely observed features, applied to the early diagnosis of dyslexia. Lect Notes Comput Sci 5271:608–615CrossRefGoogle Scholar
  16. 16.
    Sánchez L, Otero J, Couso I (2009) Obtaining linguistic fuzzy rule-based regression models from imprecise data with multiobjective genetic algorithms. Soft Comput 13(5):467–479zbMATHCrossRefGoogle Scholar
  17. 17.
    Sánchez L, Couso I, Casillas J (2009) Genetic learning of fuzzy rules based on low quality data. Fuzzy Sets Syst 160(17):2524–2552CrossRefGoogle Scholar
  18. 18.
    Teich J (2001) Pareto-front exploration with uncertain objectives. In: EMO 2001, pp 314–328Google Scholar
  19. 19.
    Öztürk M, Tsoukias A (2007) Valued Hesitation in intervals comparison. In: Proceedings of the SUM-07 conference, LNAI 4772, Springer, pp 157–170Google Scholar
  20. 20.
    Vinuessa M, Coll J (1984) Tratado de atletismo. Servicio Geográfico del Ejercito EspañolGoogle Scholar

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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