IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v17y2002i1d10.1007_s001800200089.html
   My bibliography  Save this article

Direct Minimization of Error Rates in Multivariate Classification

Author

Listed:
  • Michael C. Röhl

    (Königstein/Ts.)

  • Claus Weihs

    (Universität Dortmund)

  • Winfried Theis

    (Universität Dortmund)

Abstract

Summary We propose a computer intensive method for linear dimension reduction that minimizes the classification error directly. Simulated annealing (Bohachevsky et al. 1986), a modern optimization technique, is used to solve this problem effectively. This approach easily allows user preferences to be incorporated by means of penalty terms. Simulations and a real world example demonstrate the superiority of this optimal classification to classical discriminant analysis (McLachlan 1992). Special emphasis is given to the case when discriminant analysis collapses.

Suggested Citation

  • Michael C. Röhl & Claus Weihs & Winfried Theis, 2002. "Direct Minimization of Error Rates in Multivariate Classification," Computational Statistics, Springer, vol. 17(1), pages 29-46, March.
    • Handle: RePEc:spr:compst:v:17:y:2002:i:1:d:10.1007_s001800200089
    DOI: 10.1007/s001800200089
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s001800200089
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s001800200089?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.

    References listed on IDEAS

    as
    1. Polzehl, Jorg, 1995. "Projection pursuit discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 20(2), pages 141-157, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Luebke, Karsten & Weihs, Claus, 2004. "Generation of prediction optimal projection on latent factors by a stochastic search algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 297-310, September.
    2. Karsten Luebke & Claus Weihs, 2011. "Linear dimension reduction in classification: adaptive procedure for optimum results," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(3), pages 201-213, October.
    3. Enache, Daniel & Weihs, Claus, 2004. "Importance Assessment of Correlated Predictors in Business Cycles Classification," Technical Reports 2004,66, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

    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. Ju Ahn & Heike Hofmann & Dianne Cook, 2003. "A Projection Pursuit Method on the multidimensional squared Contingency Table," Computational Statistics, Springer, vol. 18(3), pages 605-626, September.
    2. Röhl, Michael C. & Weihs, Claus, 1999. "Direct minimization of error rates in multivariate classification," Technical Reports 1999,43, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    3. Montanari, Angela & Calo, Daniela G. & Viroli, Cinzia, 2008. "Independent factor discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3246-3254, February.
    4. Calo, Daniela G., 2007. "Gaussian mixture model classification: A projection pursuit approach," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 471-482, September.
    5. Klinke, S. & Grassmann, J., 1998. "Projection pursuit regression and neural networks," SFB 373 Discussion Papers 1998,17, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    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:compst:v:17:y:2002:i:1:d:10.1007_s001800200089. 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: 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.