• DocumentCode
    1554933
  • Title

    Combination of multiple classifiers using local accuracy estimates

  • Author

    Woods, Kevin ; Kegelmeyer, W. Philip, Jr. ; Bowyer, Kevin

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Univ. of South Florida, Tampa, FL, USA
  • Volume
    19
  • Issue
    4
  • fYear
    1997
  • fDate
    4/1/1997 12:00:00 AM
  • Firstpage
    405
  • Lastpage
    410
  • Abstract
    This paper presents a method for combining classifiers that uses estimates of each individual classifier´s local accuracy in small regions of feature space surrounding an unknown test sample. An empirical evaluation using five real data sets confirms the validity of our approach compared to some other combination of multiple classifiers algorithms. We also suggest a methodology for determining the best mix of individual classifiers
  • Keywords
    feature extraction; optimisation; pattern classification; classifier fusion; dynamic classifier selection; feature space; local accuracy estimates; multiple classifiers; pattern recognition; receiver operating characteristic; Algorithm design and analysis; Handwriting recognition; Logistics; Partitioning algorithms; Testing; Training data; Voting;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/34.588027
  • Filename
    588027
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1554933