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

Seydou Nourou Sylla 1, 2, 3 Stéphane Girard 1 Abdou Ka Diongue 3 Aldiouma Diallo 2 Cheikh Sokhna 2
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP [2007-2019] - Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019]
2 URMITE - Unité de Recherche sur les Maladies Infectieuses Tropicales Emergentes
URMITE - Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes
Abstract : In this paper, a new supervised classification method dedicated to binary data 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 they are applied to binary data. 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 hand-digit data) using 76 similarity measures.
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https://hal.inria.fr/hal-01158043
Contributor : Stephane Girard <>
Submitted on : Friday, September 25, 2015 - 12:09:25 PM
Last modification on : Thursday, July 9, 2020 - 9:44:38 AM
Document(s) archivé(s) le : Wednesday, April 26, 2017 - 7:02:32 PM

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  • HAL Id : hal-01158043, version 3

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Seydou Nourou Sylla, Stéphane Girard, Abdou Ka Diongue, Aldiouma Diallo, Cheikh Sokhna. A classification method for binary predictors combining similarity measures and mixture models. 2015. ⟨hal-01158043v3⟩

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