IDEAS home Printed from https://ideas.repec.org/a/igg/jitwe0/v7y2012i2p60-69.html
   My bibliography  Save this article

New Entropy Based Distance for Training Set Selection in Debt Portfolio Valuation

Author

Listed:
  • Tomasz Kajdanowicz

    (Wroclaw University of Technology, Poland)

  • Slawomir Plamowski

    (Wroclaw University of Technology, Poland)

  • Przemyslaw Kazienko

    (Wroclaw University of Technology, Poland)

Abstract

Choosing a proper training set for machine learning tasks is of great importance in complex domain problems. In the paper a new distance measure for training set selection is presented and thoroughly discussed. The distance between two datasets is computed using variance of entropy in groups obtained after clustering. The approach is validated using real domain datasets from debt portfolio valuation process. Eventually, prediction performance is examined.

Suggested Citation

  • Tomasz Kajdanowicz & Slawomir Plamowski & Przemyslaw Kazienko, 2012. "New Entropy Based Distance for Training Set Selection in Debt Portfolio Valuation," International Journal of Information Technology and Web Engineering (IJITWE), IGI Global, vol. 7(2), pages 60-69, April.
    • Handle: RePEc:igg:jitwe0:v:7:y:2012:i:2:p:60-69
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jitwe.2012040105
    Download Restriction: no
    ---><---

    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:igg:jitwe0:v:7:y:2012:i:2:p:60-69. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.