Advertisement

The Effect of the Arrangement of Fuzzy If-Then Rules on the Performance of On-Line Fuzzy Classification

  • Tomoharu NakashimaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9734)

Abstract

This paper presents an experimental study on the performance of online fuzzy classifiers. First, a formulation is given for the fuzzy classifier that is used in this paper. Then, online learning techniques that were proposed in the machine learning community are applied for the fuzzy classifier. As there are several parameters that should be specified by humans in the fuzzy classifiers, a series of computational experiments are conducted in order to investigate the effect of those parameters on the classification performance of the online fuzzy classifiers. It is shown that the arrangement of fuzzy if-then rules dramatically improve the on-line classification performance.

Keywords

On-line learning Fuzzy if-then rule Classification 

References

  1. 1.
    Dredze, M., Crammer, K.: Confidence-weighted linear classification. In: Proceedings of the 25th International Conference on Machine Learning, pp. 264–271 (2008)Google Scholar
  2. 2.
    Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., Singer, Y.: Online passive-aggressive algorithms. J. Mach. Learn. Res. 7, 551–585 (2006)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Ishibuchi, H., Nakashima, T., Nii, M.: Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining. Springer, Berlin (2004)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Osaka Prefecture UniversityNaka-ku, SakaiJapan

Personalised recommendations