The quantile-based classifier with variable-wise parameters
Methodology
2024-04-23 v1
Abstract
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.
Keywords
Cite
@article{arxiv.2404.13589,
title = {The quantile-based classifier with variable-wise parameters},
author = {Marco Berrettini and Christian Hennig and Cinzia Viroli},
journal= {arXiv preprint arXiv:2404.13589},
year = {2024}
}