Advertisement

Recognition Task with Feature Selection and Weighted Majority Voting Based on Interval-Valued Fuzzy Sets

  • Robert Burduk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7653)

Abstract

This paper presents the recognition algorithm with random selection of features. In the proposed procedure of classification the choice of weights is one of the main problems. In this paper we propose the weighted majority vote rule in which weights are represented by interval-valued fuzzy set (IVFS). In our approach the weights have a lower and upper membership function. The described algorithm was tested on one data set from UCI repository. The obtained results are compared with the most popular majority vote and the weighted majority vote rule.

Keywords

Ensemble of classifiers interval-valued fuzzy sets weighted majority vote 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Krupka, E., Navot, A., Tishby, N.: Learning to Select Features using their Properties. Journal of Machine Learning Research 9, 2349–2376 (2008)zbMATHGoogle Scholar
  2. 2.
    Zadeh, L.A.: Probability measures of fuzzy events. Journal of Mathematical Analysis and Applications 23, 421–427 (1968)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Goguen, J.: L-fuzzy sets. Journal of Mathematical Analysis and Applications 18(1), 145–174 (1967)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Pawlak, Z.: Rough sets and fuzzy sets. Fuzzy Sets and Systems 17, 99–102 (1985)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning - I. Information Science 8, 199–249 (1975)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Burduk, R.: Imprecise information in Bayes classifier. Pattern Analysis and Applications 15(2), 147–153 (2012)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Mitchell, H.B.: Pattern recognition using type-II fuzzy sets. Information Science 170, 409–418 (2005)CrossRefGoogle Scholar
  8. 8.
    Zeng, J., Liu, Y.-Q.: Type-2 fuzzy markov random fields and their application to handwritten chinese character recognition. IEEE Transactions on Fuzzy Systems 16(3), 747–760 (2008)CrossRefGoogle Scholar
  9. 9.
    Melin, P.: Image Processing and Pattern Recognition with Mamdani Interval Type-2 Fuzzy Inference Systems. In: Trillas, E., Bonissone, P.P., Magdalena, L., Kacprzyk, J. (eds.) Combining Experimentation and Theory. STUDFUZZ, vol. 271, pp. 179–190. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Ho, T.: The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)CrossRefGoogle Scholar
  11. 11.
    Kuncheva, L.I., Whitaker, C.J., Duin, R.P.W.: Limits on the majority vote accuracy in classifier fusion. Pattern Analysis and Applications 6, 22–31 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Zmyslony, M., Wozniak, M., Jackowski, K.: Comparative analysis of classifier fusers. International Journal of Artificial Intelligence & Applications 3(3), 95–109 (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Robert Burduk
    • 1
  1. 1.Department of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

Personalised recommendations