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Title: An ensemble method for multivariate functional data classification, with application to mouse movement trajectories Authors:  Sonja Greven - Humboldt University of Berlin (Germany) [presenting]
Amanda Fernandez-Fontelo - HU Berlin (Germany)
Felix Henninger - University of Mannheim (Germany)
Pascal Kieslich - University of Mannheim (Germany)
Frauke Kreuter - University of Mannheim (Germany)
Abstract: An ensemble method is presented for multivariate functional data classification that combines different semi-metric-based classifiers. We extend existing methods to the multivariate case and to further ensemble methods, and allow for scalar covariates. An R package implements the presented classification methods for multivariate functional data and trajectories in n dimensions. We apply our methods to the motivating application, to predict the difficulty of respondents while filling out a web survey using their computer mouse trajectories.