IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v19y2002i3p287-301.html
   My bibliography  Save this article

Rough Sets and Multivariate Statistical Classification: A Simulation Study

Author

Listed:
  • Doumpos, Michael
  • Zopounidis, Constantin

Abstract

The classification of a set of objects into predefined homogenous groups is a problem with major practical interest in many fields. Over the past two decades several non-parametric approaches have been developed to address the classification problem, originating from several scientific fields. This paper is focused on the rough sets approach and the investigation of its performance as opposed to traditional multivariate statistical classification procedures, namely the linear discriminant analysis, the quadratic discriminant analysis and the logit analysis. For this purpose an extensive Monte Carlo simulation is conducted to examine the performance of these methods under different data conditions. Copyright 2002 by Kluwer Academic Publishers

Suggested Citation

  • Doumpos, Michael & Zopounidis, Constantin, 2002. "Rough Sets and Multivariate Statistical Classification: A Simulation Study," Computational Economics, Springer;Society for Computational Economics, vol. 19(3), pages 287-301, June.
    • Handle: RePEc:kap:compec:v:19:y:2002:i:3:p:287-301
    as

    Download full text from publisher

    File URL: http://journals.kluweronline.com/issn/0927-7099/contents
    Download Restriction: Access to the full text of the articles in this series is restricted.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:19:y:2002:i:3:p:287-301. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.