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.
Otras publicaciones de la misma revista o congreso con autores/as de la Universidad de Cantabria
Fuente: TEST (2017) 26:119-142
Editorial: Springer
Año de publicación: 2017
Nº de páginas: 24
Tipo de publicación: Artículo de Revista
DOI: 10.1007/s11749-016-0502-6
ISSN: 1133-0686,1863-8260
Proyecto español: MTM2011-28657-C02-02 ; MTM2014-56235-C2-2-P
Url de la publicación: https://10.1007/s11749-016-0502-6