IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v75y1997i0p171-18810.1023-a1018955328719.html
   My bibliography  Save this article

A machine learning method for multi-expert decision support

Author

Listed:
  • Clyde Holsapple
  • Anita Lee
  • Jim Otto

Abstract

When a decision maker has access to multiple expert systems, each embodying a different expert perspective on analyzing and reasoning about the same kind of decision problem, an important consideration is which to use at what times. We address this issue with a method based on competition among the distinct expert systems (and their respective rules). We begin by reviewing prior research concerned with the coordination of multiple sources of expertise in support of decision making, pointing out potential weaknesses of the proposed methods. Next, we introduce a new coordination method based on the competitive paradigm that has been applied in machine learning. This method involves adjustments to the strengths of expert systems and to their constituent rules based on their performances. A nine-step process for adjusting strengths is described. Advantages and limitations of this new method for expert system coordination are discussed. We outline an approach to testing the coordination method and report on preliminary testing of the performance of a system employing our method versus the performance of individual experts. Copyright Kluwer Academic Publishers 1997

Suggested Citation

  • Clyde Holsapple & Anita Lee & Jim Otto, 1997. "A machine learning method for multi-expert decision support," Annals of Operations Research, Springer, vol. 75(0), pages 171-188, January.
    • Handle: RePEc:spr:annopr:v:75:y:1997:i:0:p:171-188:10.1023/a:1018955328719
    DOI: 10.1023/A:1018955328719
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/A:1018955328719
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1023/A:1018955328719?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. Eduardo R. Hruschka & Estevam R. Hruschka & Thiago F. Covões & Nelson F. F. Ebecken, 2006. "Bayesian Feature Selection for Clustering Problems," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 315-327.
    2. Yuan Li & Xiuwu Liao & Wenhong Zhao, 2009. "A rough set approach to knowledge discovery in analyzing competitive advantages of firms," Annals of Operations Research, Springer, vol. 168(1), pages 205-223, April.
    3. Kazim Topuz & Hasmet Uner & Asil Oztekin & Mehmet Bayram Yildirim, 2018. "Predicting pediatric clinic no-shows: a decision analytic framework using elastic net and Bayesian belief network," Annals of Operations Research, Springer, vol. 263(1), pages 479-499, April.
    4. Weiwei Chen & Jie Song & Leyuan Shi & Liang Pi & Peter Sun, 2013. "Data mining-based dispatching system for solving the local pickup and delivery problem," Annals of Operations Research, Springer, vol. 203(1), pages 351-370, March.
    5. Cuiqing Jiang & Zhao Wang & Ruiya Wang & Yong Ding, 2018. "Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending," Annals of Operations Research, Springer, vol. 266(1), pages 511-529, July.

    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:spr:annopr:v:75:y:1997:i:0:p:171-188:10.1023/a:1018955328719. 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.