Ensemble Quantile Classifier
This work addresses classification challenges in high-dimensional settings with skewed or heavy-tailed data, offering a more flexible method for domains like text analysis, though it appears incremental as an extension of existing quantile-based approaches.
The paper tackled the limitations of median- and quantile-based classifiers by introducing an ensemble quantile classifier that uses regularization to improve performance with high-dimensional, asymmetric, or noisy data, demonstrating gains in a simulation study and text categorization application.
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.