On Exact Feature Screening in Ultrahigh-dimensional Binary Classification

TL;DR

This paper introduces a model-free feature screening method based on energy distances for ultrahigh-dimensional binary classification, effectively identifying relevant features and pairs of variables, and demonstrating superior performance through extensive experiments.

Contribution

The paper presents a novel energy-distance-based feature screening method that can detect relevant features and variable pairs in ultrahigh-dimensional binary classification, with proven risk consistency.

Findings

Successfully retains relevant features with high probability

Identifies variable pairs with joint distribution differences

Outperforms existing methods in simulations and real data

Abstract

We propose a new model-free feature screening method based on energy distances for ultrahigh-dimensional binary classification problems. With a high probability, the proposed method retains only relevant features after discarding all the noise variables. The proposed screening method is also extended to identify pairs of variables that are marginally undetectable but have differences in their joint distributions. Finally, we build a classifier that maintains coherence between the proposed feature selection criteria and discrimination method and also establish its risk consistency. An extensive numerical study with simulated and real benchmark data sets shows clear and convincing advantages of our proposed method over the state-of-the-art methods.