• DocumentCode
    384279
  • Title

    How many classifiers do I need?

  • Author

    Schiele, Bernt

  • Author_Institution
    Comput. Sci. Dept., Eidgenossische Tech. Hochschule, Zurich, Switzerland
  • Volume
    2
  • fYear
    2002
  • fDate
    2002
  • Firstpage
    176
  • Abstract
    Combining multiple classifiers promises to increase performance and robustness of a classification task. Currently, the understanding which combination scheme should be used and the ability to quantify the expected benefit is inadequate. This paper attempts to quantify the performance and robustness gain for different combination schemes and for two classifier types. The results indicate that the combination of a small number of classifiers may already result in a substantial performance gain. Also, the increase in robustness can be substantial by combining an adequate number of classifiers.
  • Keywords
    pattern classification; probability; binary classifiers; complementary classifiers; majority vote; multiple classifiers; pattern classification; performance; probability; product-rule; redundant classifiers; robustness; sum-rule; Computer science; Computer vision; Equations; Noise robustness; Pattern recognition; Performance gain; Probability; Voting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition, 2002. Proceedings. 16th International Conference on
  • ISSN
    1051-4651
  • Print_ISBN
    0-7695-1695-X
  • Type

    conf

  • DOI
    10.1109/ICPR.2002.1048266
  • Filename
    1048266