On unifying probabilistic/fuzzy and possibilistic rejection-based classifiers

  • Carl Frélicot
Rejection in Pattern Recognition
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)

Abstract

This paper aims at unifying the presentation of two-fold rejectionbased pattern classifiers. We propose to define such a classifier as a couple of labelling and hardening functions which are independent in some way. Within this framework, crisp and probabilistic / fuzzy rejection-based classifiers are shown to be particular cases of possibilistic ones. Classifiers with no reject option remains particular cases of rejection-based ones. Examples of so-defined classifiers are presented and their ability to deal with the reject problem is shown on artificial and real data sets.

Keywords

Hardening Function Pattern Classification Label Vector Classification Area Unit Hypercube 
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.

References

  1. 1.
    Bezdek, J.C., Reichherzer, T R., Lim, G., Attikiouzel, Y: Classification with multiple prototypes. Proc. 5th IEEE International Conference on Fuzzy Systems (1996) 626–632Google Scholar
  2. 2.
    Chow, C.K., An optimum character recognition system using decision functions. IRE Transactions on Electronic Computers 6 (1957) 247–254Google Scholar
  3. 3.
    Dubuisson, B., Masson, M.H.: A statistical decision rule with incomplete knowledge about classes. Pattern Recognition 26 (1993) 155–165CrossRefGoogle Scholar
  4. 4.
    Dubuisson, B., Masson, M.H., Frélicot, C.: Some topics in using pattern recognition for system diagnosis. Engineering Simulation 13 (1996) 863–888Google Scholar
  5. 5.
    Frélicot, C.: Learning rejection thresholds for a class of fuzzy classifiers from possibilistic clustered noisy data. Proceedings of 7th International Fuzzy Systems Association Worldcongress 3 (1997) 111–116Google Scholar
  6. 6.
    Frélicot, C.: A rejection-based possibilistic classifier and its parameters learning. Proc. 7th IEEE International Conference on Fuzzy Systems (1998)Google Scholar
  7. 7.
    Fukunaga, K.: Introduction to statistical pattern recognition. Academic Press (1990) Second editionGoogle Scholar
  8. 8.
    Ha, TM.: The optimum class-selective rejection rule. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (1997) 608–615CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Carl Frélicot
    • 1
  1. 1.Laboratoire d'Informatique et d'Imagerie IndustrielleUniversité de La RochelleLa Rochelle Cedex 1France

Personalised recommendations