• DocumentCode
    2387973
  • Title

    Precision and Recall in Rough Support Vector Machines

  • Author

    Lingras, Pawan ; Butz, C.J.

  • Author_Institution
    St. Mary´´s Univ. Halifax, Halifax
  • fYear
    2007
  • fDate
    2-4 Nov. 2007
  • Firstpage
    654
  • Lastpage
    654
  • Abstract
    Rough support vector machines (RSVMs) supplement conventional support vector machines (SVMs) by providing a better representation of the boundary region. Increasing interest has been paid to the theoretical development of RSVMs, which has already lead to a modification of existing SVM implementations as RSVMs. This paper shows how to extend the use of precision and recall from a SVM implementation to a RSVM implementation. Our approach is demonstrated in practice with the help of Gist, a popular SVM implementation.
  • Keywords
    rough set theory; support vector machines; RSVM; boundary region representation; rough support vector machines; Computer science; Kernel; Mathematics; Multi-layer neural network; Multilayer perceptrons; Neural networks; Particle measurements; Set theory; Support vector machine classification; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Granular Computing, 2007. GRC 2007. IEEE International Conference on
  • Conference_Location
    Fremont, CA
  • Print_ISBN
    978-0-7695-3032-1
  • Type

    conf

  • DOI
    10.1109/GrC.2007.77
  • Filename
    4403181