IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v72y1997i0p51-7310.1023-a1018960607821.html
   My bibliography  Save this article

A framework for retrieval in case-based reasoning systems

Author

Listed:
  • Ali Reza Montazemi
  • Kalyan Moy Gupta

Abstract

A case-based reasoning (CBR) system supports decision makers when solving new decision problems (i.e., new cases) on the basis of past experience (i.e., previous cases). The effectiveness of a CBR system depends on its ability to retrieve useful previous cases. The usefulness of a previous case is determined by its similarity with the new case. Existing methodologies assess similarity by using a set of domain-specific production rules. However, production rules are brittle in ill-structured decision domains and their acquisition is complex and costly. We propose a framework of methodologies based on decision theory to assess the similarity of a new case with the previous case that allows amelioration of the deficiencies associated with the use of production rules. An empirical test of the framework in an ill-structured diagnostic decision environment shows that this framework significantly improves the retrieval performance of a CBR system. Copyright Kluwer Academic Publishers 1997

Suggested Citation

  • Ali Reza Montazemi & Kalyan Moy Gupta, 1997. "A framework for retrieval in case-based reasoning systems," Annals of Operations Research, Springer, vol. 72(0), pages 51-73, January.
    • Handle: RePEc:spr:annopr:v:72:y:1997:i:0:p:51-73:10.1023/a:1018960607821
    DOI: 10.1023/A:1018960607821
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/A:1018960607821
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1023/A:1018960607821?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. Jihao Shi & Xiao Ding & Ting Liu, 2024. "Case-Based Deduction for Entailment Tree Generation," Mathematics, MDPI, vol. 12(18), pages 1-20, September.

    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:72:y:1997:i:0:p:51-73:10.1023/a:1018960607821. 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.