IDEAS home Printed from https://ideas.repec.org/a/wsi/apjorx/v22y2005i04ns0217595905000704.html
   My bibliography  Save this article

A Methodological Comparison Between Standard And Two Stage Mixed Integer Approaches For Discriminant Analysis

Author

Listed:
  • TOSHIYUKI SUEYOSHI

    (Department of Management, New Mexico Institute of Mining and Technology, Socorro, New Mexico 87801-4796, USA)

Abstract

A standard mixed integer programming (MIP) approach is compared with a two-stage MIP approach. A computational difference between the two DA (discriminant analysis) approaches is that the former uses a single MIP model to solve various classification problems and the latter uses two MIP models that classify all observations into one of two groups or an overlap at the first stage and then reclassify the overlapped observations at the second stage. In this study, the two MIP approaches are methodologically compared and then applied to a published data set related to Japanese banks. These classification performances are assessed by four hit rates. Based upon the comparison, it is confirmed that the two-stage approach performs at least as well as the standard approach.

Suggested Citation

  • Toshiyuki Sueyoshi, 2005. "A Methodological Comparison Between Standard And Two Stage Mixed Integer Approaches For Discriminant Analysis," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 22(04), pages 513-528.
  • Handle: RePEc:wsi:apjorx:v:22:y:2005:i:04:n:s0217595905000704
    DOI: 10.1142/S0217595905000704
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0217595905000704
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1142/S0217595905000704?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

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

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Derek & Li, Shanling & Sueyoshi, Toshiyuki, 2014. "DEA environmental assessment on U.S. Industrial sectors: Investment for improvement in operational and environmental performance to attain corporate sustainability," Energy Economics, Elsevier, vol. 45(C), pages 254-267.

    More about this item

    Keywords

    Discriminant analysis; integer programming;

    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:wsi:apjorx:v:22:y:2005:i:04:n:s0217595905000704. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/apjor/apjor.shtml .

    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.