IDEAS home Printed from https://ideas.repec.org/a/prg/jnlaop/v2008y2008i4id131p37-55.html
   My bibliography  Save this article

Identifying the most Informative Variables for Decision-Making Problems - a Survey of Recent Approaches and Accompanying Problems

Author

Listed:
  • Pavel Pudil
  • Petr Somol

Abstract

We provide an overview of problems related to variable selection (also known as feature selection) techniques in decision-making problems based on machine learning with a particular emphasis on recent knowledge. Several popular methods are reviewed and assigned to a taxonomical context. Issues related to the generalization-versus-performance trade-off, inherent in currently used variable selection approaches, are addressed and illustrated on real-world examples.

Suggested Citation

  • Pavel Pudil & Petr Somol, 2008. "Identifying the most Informative Variables for Decision-Making Problems - a Survey of Recent Approaches and Accompanying Problems," Acta Oeconomica Pragensia, Prague University of Economics and Business, vol. 2008(4), pages 37-55.
    • Handle: RePEc:prg:jnlaop:v:2008:y:2008:i:4:id:131:p:37-55
    DOI: 10.18267/j.aop.131
    as

    Download full text from publisher

    File URL: http://aop.vse.cz/doi/10.18267/j.aop.131.html
    Download Restriction: free of charge
    File URL: http://aop.vse.cz/doi/10.18267/j.aop.131.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.18267/j.aop.131?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. Dernoncourt, David & Hanczar, Blaise & Zucker, Jean-Daniel, 2014. "Analysis of feature selection stability on high dimension and small sample data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 681-693.

    More about this item

    Keywords

    variable selection; feature selection; machine learning; decision rules; classification;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

    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:prg:jnlaop:v:2008:y:2008:i:4:id:131:p:37-55. 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: Stanislav Vojir (email available below). General contact details of provider: https://edirc.repec.org/data/uevsecz.html .

    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.