IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v33y2018i3d10.1007_s00180-016-0678-y.html
   My bibliography  Save this article

Learning vector quantization classifiers for ROC-optimization

Author

Listed:
  • T. Villmann

    (University of Applied Sciences Mittweida)

  • M. Kaden

    (University of Applied Sciences Mittweida)

  • W. Hermann

    (Paracelsus-Klinikum Zwickau)

  • M. Biehl

    (University Groningen)

Abstract

This paper proposes a variant of the generalized learning vector quantizer (GLVQ) optimizing explicitly the area under the receiver operating characteristics (ROC) curve for binary classification problems instead of the classification accuracy, which is frequently not appropriate for classifier evaluation. This is particularly important in case of overlapping class distributions, when the user has to decide about the trade-off between high true-positive and good false-positive performance. The model keeps the idea of learning vector quantization based on prototypes by stochastic gradient descent learning. For this purpose, a GLVQ-based cost function is presented, which describes the area under the ROC-curve in terms of the sum of local discriminant functions. This cost function reflects the underlying rank statistics in ROC analysis being involved into the design of the prototype based discriminant function. The resulting learning scheme for the prototype vectors uses structured inputs, i.e. ordered pairs of data vectors of both classes.

Suggested Citation

  • T. Villmann & M. Kaden & W. Hermann & M. Biehl, 2018. "Learning vector quantization classifiers for ROC-optimization," Computational Statistics, Springer, vol. 33(3), pages 1173-1194, September.
    • Handle: RePEc:spr:compst:v:33:y:2018:i:3:d:10.1007_s00180-016-0678-y
    DOI: 10.1007/s00180-016-0678-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-016-0678-y
    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/s00180-016-0678-y?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. Jens Keilwagen & Ivo Grosse & Jan Grau, 2014. "Area under Precision-Recall Curves for Weighted and Unweighted Data," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-13, March.
    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. Hans A. Kestler & Bernd Bischl & Matthias Schmid, 2018. "Proceedings of Reisensburg 2014–2015," Computational Statistics, Springer, vol. 33(3), pages 1125-1126, September.

    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. Kajal Lahiri & Cheng Yang, 2023. "ROC and PRC Approaches to Evaluate Recession Forecasts," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(2), pages 119-148, September.
    2. Stan Hatko, 2017. "The Bank of Canada 2015 Retailer Survey on the Cost of Payment Methods: Nonresponse," Technical Reports 107, Bank of Canada.
    3. Giambattista Albora & Matteo Straccamore & Andrea Zaccaria, 2024. "Machine learning-based similarity measure to forecast M&A from patent data," Papers 2404.07179, arXiv.org.
    4. W. Frank Lenoir & Micaela Morgado & Peter C. DeWeirdt & Megan McLaughlin & Audrey L. Griffith & Annabel K. Sangree & Marissa N. Feeley & Nazanin Esmaeili Anvar & Eiru Kim & Lori L. Bertolet & Medina C, 2021. "Discovery of putative tumor suppressors from CRISPR screens reveals rewired lipid metabolism in acute myeloid leukemia cells," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    5. Wahid-Ul-Ashraf, Akanda & Budka, Marcin & Musial, Katarzyna, 2019. "How to predict social relationships — Physics-inspired approach to link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1110-1129.

    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:33:y:2018:i:3:d:10.1007_s00180-016-0678-y. 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.