# Deep-gKnock: nonlinear group-feature selection with deep neural network

## TL;DR

Deep-gKnock is a novel method combining deep neural networks with Knockoffs to perform model-free, group-wise feature selection in high-dimensional data, enhancing interpretability and reproducibility.

## Contribution

It introduces a deep neural network-based group feature selection method that controls group-wise FDR, extending Knockoffs beyond linear models.

## Key findings

- Achieves superior power in feature selection.
- Accurately controls group-wise FDR.
- Improves interpretability of deep models.

## Abstract

Feature selection is central to contemporary high-dimensional data analysis. Grouping structure among features arises naturally in various scientific problems. Many methods have been proposed to incorporate the grouping structure information into feature selection. However, these methods are normally restricted to a linear regression setting. To relax the linear constraint, we combine the deep neural networks (DNNs) with the recent Knockoffs technique, which has been successful in an individual feature selection context. We propose Deep-gKnock (Deep group-feature selection using Knockoffs) as a methodology for model interpretation and dimension reduction. Deep-gKnock performs model-free group-feature selection by controlling group-wise False Discovery Rate (gFDR). Our method improves the interpretability and reproducibility of DNNs. Experimental results on both synthetic and real data demonstrate that our method achieves superior power and accurate gFDR control compared with state-of-the-art methods.