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

A classification method for binary predictors combining similarity measures and mixture models AbstractIn this paper, a new supervised classification method dedicated to binary predictors is proposed. Itsoriginality is to combine a model-based classification rule with similarity measures thanks to the introductionof new family of exponential kernels. Some links are established between existing similarity measureswhen applied to binary predictors. A new family of measures is also introduced to unify some of the existingliterature. The performance of the new classification method is illustrated on two real datasets (verbalautopsy data and handwritten digit data) using 76 similarity measures. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Dependence Modeling de Gruyter

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

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Publisher
de Gruyter
Copyright
© 2015 Seydou N. Sylla et al.
ISSN
2300-2298
eISSN
2300-2298
DOI
10.1515/demo-2015-0017
Publisher site
See Article on Publisher Site

Abstract

AbstractIn this paper, a new supervised classification method dedicated to binary predictors is proposed. Itsoriginality is to combine a model-based classification rule with similarity measures thanks to the introductionof new family of exponential kernels. Some links are established between existing similarity measureswhen applied to binary predictors. A new family of measures is also introduced to unify some of the existingliterature. The performance of the new classification method is illustrated on two real datasets (verbalautopsy data and handwritten digit data) using 76 similarity measures.

Journal

Dependence Modelingde Gruyter

Published: Dec 12, 2015

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