Performance Analysis and Comparison of Linear Combiners for Classifier Fusion

  • Giorgio Fumera
  • Fabio Roli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)


In this paper, we report a theoretical and experimental comparison between two widely used combination rules for classifier fusion: simple average and weighted average of classifiers outputs. We analyse the conditions which affect the difference between the performance of simple and weighted averaging and discuss the relation between these conditions and the concept of classifiers’ “imbalance”. Experiments aimed at assessing some of the theoretical results for cases where the theoretical assumptions could not be hold are reported.


Optimal Weight Decision Boundary Simple Average Individual Classifier Classifier Ensemble 
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 2002

Authors and Affiliations

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

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