IDEAS home Printed from https://ideas.repec.org/p/zbw/sfb475/200606.html
   My bibliography  Save this paper

Boosting Classifiers for Drifting Concepts

Author

Listed:
  • Scholz, Martin
  • Klinkenberg, Ralf

Abstract

This paper proposes a boosting-like method to train a classifier ensemble from data streams. It naturally adapts to concept drift and allows to quantify the drift in terms of its base learners. The algorithm is empirically shown to outperform learning algorithms that ignore concept drift. It performs no worse than advanced adaptive time window and example selection strategies that store all the data and are thus not suited for mining massive streams.

Suggested Citation

  • Scholz, Martin & Klinkenberg, Ralf, 2006. "Boosting Classifiers for Drifting Concepts," Technical Reports 2006,06, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:200606
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/22652/1/tr06-06.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Heilemann, Ullrich & Münch, Heinz Josef, 1999. "Classification of west german business cycles," Technical Reports 1999,11, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:zbw:rwirep:0068 is not listed on IDEAS
    2. Oliver Holtemöller & Torsten Schmidt, 2008. "Identifying Sources of Business Cycle Fluctuations in Germany 1975–1998," Ruhr Economic Papers 0068, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    3. Pumplün, Constanze & Weihs, Claus & Preusser, Andrea, 2004. "Experimental Design for Variable Selection in data bases," Technical Reports 2004,72, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    4. Ulrich FRITSCHE & Vladimir KOUZINE, 2010. "Prediction of Business Cycle Turning Points in Germany," EcoMod2004 330600054, EcoMod.
    5. Holtemöller, Oliver & Schmidt, Torsten, 2008. "Identifying Sources of Business Cycle Fluctuations in Germany 1975–1998," Ruhr Economic Papers 68, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

    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:zbw:sfb475:200606. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/isdorde.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.