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A New Heuristic Measure for Learning Rules from Fuzzy Data

  • Francisco Botana
  • José Ranilla
  • Ricardo Mones
  • Antonio Bahamonde
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 5)

Abstract

Decision tree induction and rule production methods have been proven as efficient tools in concept learning or data mining tasks. These approaches exhibit a good performance even when there are cognitive uncertainties in the data. Most systems in this paradigm use the information gain criterion in selecting attributes when learning. This paper presents an alternative approach. A heuristic measure of the impurity level of rules when dealing with fuzzy data is described and used in a classification algorithm. Results on the Sports classification problem are reported and compared with those of other learning algorithms.

Keywords

Impurity Level Rule Induction Fuzzy Data Data Mining Task High Membership 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Quinlan, J. R. (1986) Induction of decision trees. Mach. Learning 1, 81–106Google Scholar
  2. 2.
    Cendrowska, J. (1988) PRISM: an algorithm for inducing modular rules. Int. J. Man-Mach. Stud. 27, 349–370CrossRefGoogle Scholar
  3. 3.
    Yuan, Y., Shaw, M. J. (1995) Induction of fuzzy decision trees. Fuzzy Sets Syst. 69, 125–139MathSciNetCrossRefGoogle Scholar
  4. 4.
    Wang, C. H., Liu, J. F. et al. (1999) A fuzzy inductive learning strategy for modular rules. Fuzzy Sets Syst. 103, 91–105CrossRefGoogle Scholar
  5. 5.
    Ranilla, J., Mones, R. et al. (1997) El nivel de impureza de una regia de clasificación aprendida a partir de ejemplos. Proc. VII Conf. Asoc. Esp. Int. Art. AEPIA, Torremolinos, Spain, 479–488Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Francisco Botana
    • 1
  • José Ranilla
    • 2
  • Ricardo Mones
    • 2
  • Antonio Bahamonde
    • 2
  1. 1.Universidad de Vigo, EUETFPontevedraSpain
  2. 2.Centro de Inteligencia ArtificialUniversidad de Oviedo en GijónGijónSpain

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