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Error Rejection in Linearly Combined Multiple Classifiers

  • Giorgio Fumera
  • Fabio Roli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2096)

Abstract

In this paper, the error-reject trade-off of linearly combined multiple classifiers is analysed in the framework of the minimum risk theory. Theoretical analysis described in [12,13] is extended for handling reject option and the optimality of the error-reject trade-off is analysed under the assumption of independence among the errors of the individual classifiers. Improvements of the error-reject trade-off obtained by linear classifier combination are quantified. Finally, a method for computing the coefficients of the linear combination and the value of the reject threshold is proposed. Experimental results on four different data sets are reported.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Giorgio Fumera
    • 1
  • Fabio Roli
    • 1
  1. 1.Dept. of Electrical and Electronic Eng.University of CagliariCagliariItaly

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