Combinations of multiple classifiers using fuzzy sets

  • Ludmila I. Kuncheva
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 49)


Different classifiers can be built using the labeled data set Z. Instead of choosing for further use the classifier with the best accuracy, we can keep a set of them. Let D = {D1,..., DL} be a set of L classifiers designed on the data set Z. The idea is to combine their outputs hoping to increase the accuracy beyond that of the best classifier in the pool D. This is a theoretically justified hope as we show later but there is no guarantee that picking an arbitrary set of classifiers will render a successful team. Combining classifiers has been an important research topic coming under different names in the literature:
  • combination of multiple classifiers [172, 209, 282, 350, 352];

  • classifier fusion [62, 102, 115, 164] ;

  • mixture of experts [150, 151, 156, 256];

  • committees of neural networks [43, 79] ;

  • consensus aggregation [28, 29, 252];

  • voting pool of classifiers [24];

  • dynamic classifier selection [350];

  • composite classifier system [70];

  • classifier ensembles [79, 95];

  • divide-and-conquer classifiers [61];

  • pandemonium system of reflective agents [309];

  • change-glasses approach to classifier selection [189], etc.


Multiple Classifier Individual Classifier Aggregation Rule Fusion Scheme Classifier Output 
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 2000

Authors and Affiliations

  • Ludmila I. Kuncheva
    • 1
  1. 1.School of InformaticsUniversity of WalesBangor GwyneddUK

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