Cuesta Albertos, Juan A.Febrero Bande, ManuelOviedo de la Fuente, Manuel2019-04-172019-04-172017Cuesta-Albertos, J.A., Febrero-Bande, M. & Oviedo de la Fuente, M. TEST (2017) 26: 119. https://doi.org/10.1007/s11749-016-0502-61133-0686http://hdl.handle.net/10347/18647This is a post-peer-review, pre-copyedit version of an article published in TEST. The final authenticated version is available online at: https://doi.org/10.1007/s11749-016-0502-6The 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 setseng© Sociedad de Estadística e Investigación Operativa 2016DD-classifierFunctional depthsFunctional data analysisjournal article10.1007/s11749-016-0502-61863-8260open access