A classification method for binary predictors combining similarity measures and mixture models - Archive ouverte HAL Access content directly
Journal Articles Dependence Modeling Year : 2015

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

(1, 2, 3, 4) , (4, 1) , (3, 4) , (2, 4) , (2)
1
2
3
4

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.
Fichier principal
Vignette du fichier
review_article_statistical2.pdf (552.75 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

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

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⟩
441 View
568 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More