IDEAS home Printed from https://ideas.repec.org/a/wly/isacfm/v3y1994i4p263-278.html
   My bibliography  Save this article

Analysis of the Effectiveness of Machine Learning in Determining Decision Rules for Executive Compensation Planning

Author

Listed:
  • Robert H. Michaelsen
  • Kathleen M. Swigger

Abstract

This paper describes the use of a machine‐learning technique to derive executive compensation planning rules. The specific learning technique was AQ15, which constructs rules by classifying sample data according to features or characteristics. The validity of the resulting rules was tested by obtaining a measure of performance called an estimate of the error rate. The machine‐derived rules were then compared to rules made by human experts and by two other versions of AQ15. Although not statistically significant, the machine‐learning version of AQ15 using hypotheses from human experts outperformed the other methods of rule construction.

Suggested Citation

  • Robert H. Michaelsen & Kathleen M. Swigger, 1994. "Analysis of the Effectiveness of Machine Learning in Determining Decision Rules for Executive Compensation Planning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 3(4), pages 263-278, December.
    • Handle: RePEc:wly:isacfm:v:3:y:1994:i:4:p:263-278
    DOI: 10.1002/j.1099-1174.1994.tb00070.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/j.1099-1174.1994.tb00070.x
    Download Restriction: no
    File URL: https://libkey.io/10.1002/j.1099-1174.1994.tb00070.x?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
    ---><---

    Citations

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


    Cited by:

    1. Ali Mirza Mahmood & Mrithyumjaya Rao Kuppa, 2012. "A Novel Approach using Expert Knowledge on Error based Pruning," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 1-11.

    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:wly:isacfm:v:3:y:1994:i:4:p:263-278. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1099-1174/ .

    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.