  We propose new ensemble models for multivariate functional data
classification as combinations of semi-metric-based weak learners. Our models
extend current semi-metric-type methods from the univariate to the multivariate
case, propose new semi-metrics to compute distances between functions, and
consider more flexible options for combining weak learners using stacked
generalisation methods. We apply these ensemble models to identify respondents'
difficulty with survey questions, with the aim to improve survey data quality.
As predictors of difficulty, we use mouse movement trajectories from the
respondents' interaction with a web survey, in which several questions were
manipulated to create two scenarios with different levels of difficulty.
