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

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 simi...

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Bibliographic Details
Published in:Dependence modeling 2015-12, Vol.3 (1), p.240-255
Main Authors: Sylla, Seydou N., Girard, Stéphane, Diongue, Abdou Ka, Diallo, Aldiouma, Sokhna, Cheikh
Format: Article
Language:English
Subjects:
binary predictors
kernel method
Mathematics
Mixture model
similarity measure
Statistics
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Summary: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.
ISSN:2300-2298
2300-2298
DOI:10.1515/demo-2015-0017