Deep Feature Screening: Feature Selection for Ultra High-Dimensional Data via Deep Neural Networks

Abstract

The applications of traditional statistical feature selection methods to high-dimension, lowsample-size data often struggle and encounter challenging problems, such as overfitting, curse of dimensionality, computational infeasibility, and strong model assumption. In this paper, we propose a novel two-step nonparametric approach called Deep Feature Screening (DeepFS) that can overcome these problems and identify significant features with high precision for ultra high-