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

Seydou N. Sylla; Stéphane Girard; Abdou Ka Diongue; Aldiouma Diallo; Cheikh Sokhna

Dependence Modeling (2015)

  • Volume: 3, Issue: 1
  • ISSN: 2300-2298

Abstract

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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.

How to cite

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Seydou N. Sylla, et al. "A classification method for binary predictors combining similarity measures and mixture models." Dependence Modeling 3.1 (2015): null. <http://eudml.org/doc/275918>.

@article{SeydouN2015,
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.},
author = {Seydou N. Sylla, Stéphane Girard, Abdou Ka Diongue, Aldiouma Diallo, Cheikh Sokhna},
journal = {Dependence Modeling},
keywords = {Mixture model; binary predictors; kernel method; similarity measure},
language = {eng},
number = {1},
pages = {null},
title = {A classification method for binary predictors combining similarity measures and mixture models},
url = {http://eudml.org/doc/275918},
volume = {3},
year = {2015},
}

TY - JOUR
AU - Seydou N. Sylla
AU - Stéphane Girard
AU - Abdou Ka Diongue
AU - Aldiouma Diallo
AU - Cheikh Sokhna
TI - A classification method for binary predictors combining similarity measures and mixture models
JO - Dependence Modeling
PY - 2015
VL - 3
IS - 1
SP - null
AB - 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.
LA - eng
KW - Mixture model; binary predictors; kernel method; similarity measure
UR - http://eudml.org/doc/275918
ER -

References

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