# On Exact Feature Screening in Ultrahigh-dimensional Binary Classification

arXiv: https://arxiv.org/abs/2205.03831 · HTML: https://mlsanity.com/paper/2205.03831/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2022.

## Built on by (most cited first, 3 of 3)

- [Feature space reduction method for ultrahigh-dimensional, multiclass data: Random forest-based multiround screening (RFMS)](https://mlsanity.com/paper/2305.15793/) (2023)
- [Robust Classification of High-Dimensional Data using Data-Adaptive Energy Distance](https://mlsanity.com/paper/2306.13985/) (2023)
- [Classification of High-dimensional Time Series in Spectral Domain using Explainable Features](https://mlsanity.com/paper/2408.08388/) (2024)

## Abstract (arXiv)

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.