A classification method for binary predictors combining similarity measures and mixture models
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.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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A classification method for binary predictors combining similarity measures and mixture models ; volume:3 ; number:1 ; year:2015 ; extent:16
Dependence modeling ; 3, Heft 1 (2015) (gesamt 16)
- Creator
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Sylla, Seydou N.
Girard, Stéphane
Diongue, Abdou Ka
Diallo, Aldiouma
Sokhna, Cheikh
- DOI
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10.1515/demo-2015-0017
- URN
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urn:nbn:de:101:1-2411181548461.729751570890
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:34 AM CEST
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Associated
- Sylla, Seydou N.
- Girard, Stéphane
- Diongue, Abdou Ka
- Diallo, Aldiouma
- Sokhna, Cheikh
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