The quantile‐based classifier with variable‐wise parameters

Abstract Quantile‐based classifiers can classify high‐dimensional observations by minimizing 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.