• DocumentCode
    2345190
  • Title

    Non-parametric discriminatory power

  • Author

    Holz, Hilary J. ; Loew, Murray H.

  • Author_Institution
    Dept. of Electr. Eng. & Comput. Sci., George Washington Univ., Washington, DC, USA
  • fYear
    1994
  • fDate
    27-29 Oct 1994
  • Firstpage
    65
  • Abstract
    Discriminatory power is the relative usefulness of a feature for classification. Traditionally feature-selection techniques have defined discriminatory power in terms of a particular classifier. Non-parametric discriminately power allows feature selection to be based on the structure of the data rather than on the requirements of any one classifier. In previous research, we have defined a metric for non-parametric discriminatory power called relative feature importance (RFI). In this work, we explore the construction of RFI through closed-form analysis and experimentation. The behavior of RFI is also compared to traditional techniques
  • Keywords
    data structures; feature extraction; pattern classification; closed-form analysis; data structure; experimentation; feature classification; feature selection techniques; nonparametric discriminatory power; relative feature importance; Algorithm design and analysis; Computer science; Data mining; Diseases; Eigenvalues and eigenfunctions; Feature extraction; Radiofrequency interference; Scattering; System testing; Zinc;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on
  • Conference_Location
    Alexandria, VA
  • Print_ISBN
    0-7803-2761-6
  • Type

    conf

  • DOI
    10.1109/WITS.1994.513894
  • Filename
    513894