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

Sylla Seydou N., Girard Stéphane, Abdou Ka Diongue, Diallo Aldiouma and Sokhna Cheikh
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Sylla Seydou N.: Inria Grenoble Rhône-Alpes & LJK, France
Girard Stéphane: Inria Grenoble Rhône-Alpes & LJK, France
Diallo Aldiouma: URMITE-IRD, Dakar, Sénégal
Sokhna Cheikh: URMITE-IRD, Dakar, Sénégal

Dependence Modeling, 2015, vol. 3, issue 1, 16

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.

Keywords: Mixture model; binary predictors; kernel method; similarity measure (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:demode:v:3:y:2015:i:1:p:16:n:17

DOI: 10.1515/demo-2015-0017

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