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 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

similarity measure Mixture model binary data kernel method

Domains

Mathematics [math] Statistics [math.ST]

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review_article_statistical2.pdf (552.75 Ko)

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Stephane Girard : Connect in order to contact the contributor

https://hal.inria.fr/hal-01158043

Submitted on : Friday, April 22, 2016-9:28:33 AM

Last modification on : Wednesday, December 14, 2022-11:50:03 AM

Dates and versions

hal-01158043 , version 1 (29-05-2015)

hal-01158043 , version 2 (11-06-2015)

hal-01158043 , version 3 (25-09-2015)

hal-01158043 , version 4 (20-11-2015)

hal-01158043 , version 5 (22-04-2016)

Identifiers

HAL Id : hal-01158043 , version 5
DOI : 10.1515/demo-2015-0017

Cite

Seydou Nourou Sylla, Stéphane Girard, Abdou Ka Diongue, Aldiouma Diallo, Cheikh Sokhna. A classification method for binary predictors combining similarity measures and mixture models. Dependence Modeling, 2015, 3, pp.240-255. ⟨10.1515/demo-2015-0017⟩. ⟨hal-01158043v5⟩

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