IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v163y2008i1p5-1810.1007-s10479-008-0325-2.html
   My bibliography  Save this article

Using genetic algorithms to optimize nearest neighbors for data mining

Author

Listed:
  • Hyunchul Ahn
  • Kyoung-jae Kim

Abstract

Case-based reasoning (CBR) is widely used in data mining for managerial applications because it often shows significant promise for improving the effectiveness of complex and unstructured decision making. There are, however, some limitations in designing appropriate case indexing and retrieval mechanisms including feature selection and feature weighting. Some of the prior studies pointed out that finding the optimal k parameter for the k-nearest neighbor (k-NN) is also one of the most important factors for designing an effective CBR system. Nonetheless, there have been few attempts to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. This study proposes a genetic algorithm (GA) approach to optimize the number of neighbors to combine. In this study, we apply this novel model to two real-world cases involving stock market and online purchase prediction problems. Experimental results show that a GA-optimized k-NN approach may outperform traditional k-NN. In addition, these results also show that our proposed method is as good as or sometime better than other AI techniques in performance-comparison. Copyright Springer Science+Business Media, LLC 2008

Suggested Citation

  • Hyunchul Ahn & Kyoung-jae Kim, 2008. "Using genetic algorithms to optimize nearest neighbors for data mining," Annals of Operations Research, Springer, vol. 163(1), pages 5-18, October.
    • Handle: RePEc:spr:annopr:v:163:y:2008:i:1:p:5-18:10.1007/s10479-008-0325-2
    DOI: 10.1007/s10479-008-0325-2
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-008-0325-2
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10479-008-0325-2?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. Şenay Yaşar Sağlam & W. Nick Street, 2018. "Distant diversity in dynamic class prediction," Annals of Operations Research, Springer, vol. 263(1), pages 5-19, April.
    2. Andrew Kusiak & Xiupeng Wei, 2014. "Prediction of methane production in wastewater treatment facility: a data-mining approach," Annals of Operations Research, Springer, vol. 216(1), pages 71-81, May.
    3. Konstantin Kogan & Avi Herbon & Beatrice Venturi, 2020. "Direct marketing of an event under hazards of customer saturation and forgetting," Annals of Operations Research, Springer, vol. 295(1), pages 207-227, December.
    4. Filipa Fernandes & Charalampos Stasinakis & Zivile Zekaite, 2019. "Forecasting government bond spreads with heuristic models: evidence from the Eurozone periphery," Annals of Operations Research, Springer, vol. 282(1), pages 87-118, November.

    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:163:y:2008:i:1:p:5-18:10.1007/s10479-008-0325-2. 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.