• DocumentCode
    680740
  • Title

    Enhancing Classification Accuracy with the Help of Feature Maximization Metric

  • Author

    Lamirel, Jean-Charles

  • Author_Institution
    Synalp Team, LORIA, Vandœuvre-lès-Nancy, France
  • fYear
    2013
  • fDate
    4-6 Nov. 2013
  • Firstpage
    569
  • Lastpage
    574
  • Abstract
    This paper deals with a new feature selection and feature contrasting approach for enhancing classification of both numerical and textual data. The method is experienced on different types of reference datasets. The paper illustrates that the proposed approach provides a very significant performance increase in all the studied cases clearly figuring out its generic character.
  • Keywords
    feature selection; optimisation; pattern classification; classification accuracy enhancement; feature contrasting approach; feature maximization metric; feature selection; numerical data; reference datasets; textual data; Accuracy; Classification algorithms; Context; Measurement; Niobium; Principal component analysis; Standards; classification; feature maximization; feature selection; numerical data; text;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence (ICTAI), 2013 IEEE 25th International Conference on
  • Conference_Location
    Herndon, VA
  • ISSN
    1082-3409
  • Print_ISBN
    978-1-4799-2971-9
  • Type

    conf

  • DOI
    10.1109/ICTAI.2013.90
  • Filename
    6735301
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=680740