• DocumentCode
    1521030
  • Title

    Embedded Feature Ranking for Ensemble MLP Classifiers

  • Author

    Windeatt, Terry ; Duangsoithong, Rakkrit ; Smith, Raymond

  • Author_Institution
    Centre for Vision Speech & Signal Process., Univ. of Surrey, Guildford, UK
  • Volume
    22
  • Issue
    6
  • fYear
    2011
  • fDate
    6/1/2011 12:00:00 AM
  • Firstpage
    988
  • Lastpage
    994
  • Abstract
    A feature ranking scheme for multilayer perceptron (MLP) ensembles is proposed, along with a stopping criterion based upon the out-of-bootstrap estimate. To solve multi-class problems feature ranking is combined with modified error-correcting output coding. Experimental results on benchmark data demonstrate the versatility of the MLP base classifier in removing irrelevant features.
  • Keywords
    learning (artificial intelligence); multilayer perceptrons; pattern classification; MLP base classifier; embedded feature ranking; error-correcting output coding; multiclass problem; multilayer perceptron ensemble; stopping criterion; Boosting; Decoding; Encoding; Error analysis; Noise; Static VAr compensators; Training; Classification; multilayer perceptrons; pattern analysis; pattern recognition; Algorithms; Computer Simulation; Models, Theoretical; Neural Networks (Computer); Pattern Recognition, Automated;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/TNN.2011.2138158
  • Filename
    5771118
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1521030