IDEAS home Printed from
   My bibliography  Save this article

An investigation of TREPAN utilising a continuous oracle model


  • William A. Young II
  • Gary R. Weckman
  • Maimuna H. Rangwala
  • Harry S. Whiting II
  • Helmut W. Paschold
  • Andrew H. Snow
  • Chad L. Mourning


TREPAN is decision tree algorithm that utilises artificial neural networks (ANNs) in order to improve partitioning conditions when sample data is sparse. When sample sizes are limited during the tree-induction process, TREPAN relies on an ANN oracle in order to create artificial sample instances. The original TREPAN implementation was limited to ANNs that were designed to be classification models. In other words, TREPAN was incapable of building decision trees from ANN models that were continuous in nature. Thus, the objective of this research was to modify the original implementation of TREPAN in order to develop and test decision trees derived from continuous-based ANN models. Though the modification were minor, they are significant because it provides researchers and practitioners an additional strategy to extract knowledge from a trained ANN regardless of its design. This research also explores how TEPAN's adjustable settings influence predictive performances based on a dataset's complexity and size.

Suggested Citation

  • William A. Young II & Gary R. Weckman & Maimuna H. Rangwala & Harry S. Whiting II & Helmut W. Paschold & Andrew H. Snow & Chad L. Mourning, 2011. "An investigation of TREPAN utilising a continuous oracle model," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 3(4), pages 325-352.
  • Handle: RePEc:ids:injdan:v:3:y:2011:i:4:p:325-352

    Download full text from publisher

    File URL:
    Download Restriction: Access to full text is restricted to subscribers.

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


    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:ids:injdan:v:3:y:2011:i:4:p:325-352. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Darren Simpson). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.