The quantile-based classifier with variable-wise parameters

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 introduc...

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Hauptverfasser:	Berrettini, Marco, Hennig, Christian, Viroli, Cinzia
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Sprache:	eng
Schlagworte:	Statistics - Methodology
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creator	Berrettini, Marco Hennig, Christian Viroli, Cinzia
description	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.
doi_str_mv	10.48550/arxiv.2404.13589
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title	The quantile-based classifier with variable-wise parameters
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