Application of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority Voting

  • Dymitr Ruta
  • Bogdan Gabrys
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2096)


In many pattern recognition tasks, an approach based on combining classifiers has shown a significant potential gain in comparison to the performance of an individual best classifier. This improvement turned out to be subject to a sufficient level of diversity exhibited among classifiers, which in general can be assumed as a selective property of classifier subsets. Given a large number of classifiers, an intelligent classifier selection process becomes a crucial issue of multiple classifier system design. In this paper, we have investigated three evolutionary optimization methods for the classifier selection task. Based on our previous studies of various diversity measures and their correlation with majority voting error we have adopted majority voting performance computed for the validation set directly as a fitness function guiding the search. To prevent from training data overfitting we extracted a population of best unique classifier combinations, and used them for second stage majority voting. In this work we intend to show empirically, that using efficient evolutionary-based selection leads to the results comparable to absolutely best, found exhaustively. Moreover, as we showed for selected datasets, introducing a second stage combining by majority voting has the potential for both, further improvement of the recognition rate and increase of the reliability of combined outputs.


Genetic Algorithm Evolutionary Algorithm Tabu Search Majority Vote Probability Vector 
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

  • Dymitr Ruta
    • 1
  • Bogdan Gabrys
    • 1
  1. 1.Applied Computational Intelligence Research Unit, Division of Computing and Information SystemsUniversity of PaisleyPaisleyUK

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