Methods for Designing Multiple Classifier Systems

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


In the field of pattern recognition, multiple classifier systems based on the combination of outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. In this paper, the problem of design of multiple classifier system is discussed. Six design methods based on the so-called “overproduce and choose“ paradigm are described and compared by experiments. Although these design methods exhibited some interesting features, they do not guarantee to design the optimal multiple classifier system for the classification task at hand. Accordingly, the main conclusion of this paper is that the problem of the optimal MCS design still remains open.


Heuristic Rule Classifier Ensemble Radial Basis Function Combination Function Generalisation Diversity 
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 2001

Authors and Affiliations

  • Fabio Roli
    • 1
  • Giorgio Giacinto
    • 1
  • Gianni Vernazza
    • 2
  1. 1.Department of Electrical and Electronic EngineeringUniversity of CagliariCagliariItaly
  2. 2.Dept. of Byophisical and Electronic Eng.University of GenoaGenoaItaly

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