• DocumentCode
    475895
  • Title

    A new measure of classifier diversity in multiple classifier system

  • Author

    Fan, Tie-gang ; Zhu, Ying ; Chen, Jun-Min

  • Author_Institution
    Fac. of Math. & Comput. Sci., Hebei Univ., Baoding
  • Volume
    1
  • fYear
    2008
  • fDate
    12-15 July 2008
  • Firstpage
    18
  • Lastpage
    21
  • Abstract
    Diversity among the team has been recognized as a very important characteristic in classifier combination. There are varied diversity measures. They can be categorized into two types, pairwise diversity measures and non-pairwise diversity measures. Above diversity measures are defined based on Oracle outputs of classifier. While using diversity measures to calculate diversity of classifiers that have soft label outputs, much information about class will be lost. That is a weakness of above measures. In order to solve the problem, this paper puts forward a new diversity measure, which can be used in the classifiers that have soft label outputs. Experimental results show that it contains more information about classifier outputs and accurately reflects the difference of classifier outputs.
  • Keywords
    pattern classification; Oracle outputs; classifier diversity; multiple classifier system; soft label outputs; Character recognition; Computer science; Cybernetics; Educational institutions; Entropy; Loss measurement; Machine learning; Mathematics; Q measurement; Statistics; Diversity measures; MCS; Oracle output; Soft label output;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2008 International Conference on
  • Conference_Location
    Kunming
  • Print_ISBN
    978-1-4244-2095-7
  • Electronic_ISBN
    978-1-4244-2096-4
  • Type

    conf

  • DOI
    10.1109/ICMLC.2008.4620371
  • Filename
    4620371