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The \(\hbox {DD}^G\)-classifier in the functional setting

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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 these methods; 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.

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Acknowledgments

This research was partially supported by the Spanish Ministerio de Ciencia y Tecnología, Grants MTM2011-28657-C02-02, MTM2014-56235-C2-2-P (J.A. Cuesta–Albertos) and MTM2013-41383-P (M. Febrero–Bande and M. Oviedo de la Fuente).

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Authors and Affiliations

  1. Department of Mathematics, Statistics and Computation, University of Cantabria, Santander, Spain

    J. A. Cuesta-Albertos

  2. Department of Statistics and Operations Research, University of Santiago de Compostela, Santiago de Compostela, Spain

    M. Febrero-Bande & M. Oviedo de la Fuente

Authors
  1. J. A. Cuesta-Albertos
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  2. M. Febrero-Bande
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  3. M. Oviedo de la Fuente
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Corresponding author

Correspondence to M. Oviedo de la Fuente.

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Cuesta-Albertos, J.A., Febrero-Bande, M. & Oviedo de la Fuente, M. The \(\hbox {DD}^G\)-classifier in the functional setting. TEST 26, 119–142 (2017). https://doi.org/10.1007/s11749-016-0502-6

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