  Quantile-based classifiers can classify high-dimensional observations by
minimising a discrepancy of an observation to a class based on suitable
quantiles of the within-class distributions, corresponding to a unique
percentage for all variables. The present work extends these classifiers by
introducing a way to determine potentially different optimal percentages for
different variables. Furthermore, a variable-wise scale parameter is
introduced. A simple greedy algorithm to estimate the parameters is proposed.
Their consistency in a nonparametric setting is proved. Experiments using
artificially generated and real data confirm the potential of the
quantile-based classifier with variable-wise parameters.
