IDEAS home Printed from https://ideas.repec.org/a/vrs/demode/v3y2015i1p16n17.html
   My bibliography  Save this article

A classification method for binary predictors combining similarity measures and mixture models

Author

Listed:
  • Sylla Seydou N.

    (Inria Grenoble Rhône-Alpes & LJK, France)

  • Girard Stéphane

    (Inria Grenoble Rhône-Alpes & LJK, France)

  • Diongue Abdou Ka

    (LERSTAD-UGB, Saint-Louis, Sénégal)

  • Diallo Aldiouma

    (URMITE-IRD, Dakar, Sénégal)

  • Sokhna Cheikh

    (URMITE-IRD, Dakar, Sénégal)

Abstract

In this paper, a new supervised classification method dedicated to binary predictors is proposed. Its originality is to combine a model-based classification rule with similarity measures thanks to the introduction of new family of exponential kernels. Some links are established between existing similarity measures when applied to binary predictors. A new family of measures is also introduced to unify some of the existing literature. The performance of the new classification method is illustrated on two real datasets (verbal autopsy data and handwritten digit data) using 76 similarity measures.

Suggested Citation

  • Sylla Seydou N. & Girard Stéphane & Diongue Abdou Ka & Diallo Aldiouma & Sokhna Cheikh, 2015. "A classification method for binary predictors combining similarity measures and mixture models," Dependence Modeling, De Gruyter, vol. 3(1), pages 1-16, December.
    • Handle: RePEc:vrs:demode:v:3:y:2015:i:1:p:16:n:17
    DOI: 10.1515/demo-2015-0017
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/demo-2015-0017
    Download Restriction: no
    File URL: https://libkey.io/10.1515/demo-2015-0017?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
    ---><---

    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:vrs:demode:v:3:y:2015:i:1:p:16:n:17. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.