Directional quantile classifiers

TL;DR

This paper introduces directional quantile classifiers that adaptively select optimal quantile levels and directions, demonstrating near-perfect classification in high-dimensional settings through theoretical analysis and empirical validation.

Contribution

It presents a novel classification method based on directional quantiles, with theoretical results for optimal selection and divergence rates, and provides practical implementation in R.

Findings

Misclassification rate approaches zero under certain distribution conditions.

Method performs well in both small and high-dimensional data.

Code is available in the R package Qtools.

Abstract

We introduce classifiers based on directional quantiles. We derive theoretical results for selecting optimal quantile levels given a direction, and, conversely, an optimal direction given a quantile level. We also show that the misclassification rate is infinitesimal if population distributions differ by at most a location shift and if the number of directions is allowed to diverge at the same rate of the problem's dimension. We illustrate the satisfactory performance of our proposed classifiers in both small and high dimensional settings via a simulation study and a real data example. The code implementing the proposed methods is publicly available in the R package Qtools.