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

Author

Listed:
  • Sylla Seydou N.
  • Girard Stéphane

    (Inria Grenoble Rhône-Alpes & LJK, France)

  • Diongue Abdou Ka

    (LERSTAD-UGB, Saint-Louis, Sénégal)

  • Diallo Aldiouma
  • 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
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