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Estimating the Serial Combination’s Performance from That of Individual Base Classifiers

  • Gian Luca Marcialis
  • Luca Didaci
  • Fabio Roli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)

Abstract

Although the large number of MCS topics, serial fusion of multiple classifiers has been poorly investigated so far. In this paper, we propose a model which, starting from the performance of individual classifiers and the traditional hypothesis of decision independence given the class, is able to estimate the performance, in terms of error rates, of the whole serial classification scheme. The model is tested on a large set of data sets and classifiers, and the importance of the basis hypothesis is evaluated under different scenarios, which can be in agreement or not with such hypothesis.

Keywords

Serial System Biometric System Sequential Probability Ratio Test Rejection Region Random Split 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gian Luca Marcialis
    • 1
  • Luca Didaci
    • 1
  • Fabio Roli
    • 1
  1. 1.Dept. of Electrical and Electronic EngineeringUniversity of CagliariCagliariItaly

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