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Abstract: The maximum depth classifier was the first attempt to use data depths instead of multivariate raw data in classification problems. Recently, the DD-classifier has addressed some of the serious limitations of this classifier but issues still remain. This paper aims to extend the DD-classifier as follows: first, by enabling it to handle more than two groups; second, by applying regular classification methods (such as kNN, linear or quadratic classifiers, recursive partitioning, etc) to DD-plots, which is particularly useful, because it gives insights based on the diagnostics of thesemethods; and third, by integrating various sources of information (data depths, multivariate functional data, etc) in the classification procedure in a unified way. This paper also proposes an enhanced revision of several functional data depths and it provides a simulation study and applications to some real data sets.
Fuente: TEST (2017) 26:119-142
Publisher: Springer
Year of publication: 2017
No. of pages: 24
Publication type: Article
DOI: 10.1007/s11749-016-0502-6
ISSN: 1133-0686,1863-8260
Spanish project: MTM2011-28657-C02-02 ; MTM2014-56235-C2-2-P
Publication Url: https://10.1007/s11749-016-0502-6
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JUAN ANTONIO CUESTA ALBERTOS
FEBRERO-BANDE, M.
OVIEDO DE LA FUENTE, M.
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