Ensemble Quantile Classifier

TL;DR

The paper introduces an ensemble quantile classifier that improves high-dimensional data classification by incorporating regularization, demonstrating superior performance over traditional median and quantile classifiers in simulations and text categorization.

Contribution

It presents a novel regularized ensemble quantile classifier that enhances discrimination in high-dimensional, skewed, or irrelevant input scenarios, with theoretical consistency and optimality proofs.

Findings

Outperforms traditional classifiers in simulations

Achieves better accuracy in text categorization

Proven to be Bayes optimal under certain conditions

Abstract

Both the median-based classifier and the quantile-based classifier are useful for discriminating high-dimensional data with heavy-tailed or skewed inputs. But these methods are restricted as they assign equal weight to each variable in an unregularized way. The ensemble quantile classifier is a more flexible regularized classifier that provides better performance with high-dimensional data, asymmetric data or when there are many irrelevant extraneous inputs. The improved performance is demonstrated by a simulation study as well as an application to text categorization. It is proven that the estimated parameters of the ensemble quantile classifier consistently estimate the minimal population loss under suitable general model assumptions. It is also shown that the ensemble quantile classifier is Bayes optimal under suitable assumptions with asymmetric Laplace distribution inputs.