Feature Selection Based on Sparse Neural Network Layer with Normalizing Constraints

TL;DR

This paper introduces a neural network-based feature selection method with normalization constraints, effectively identifying important features in high-dimensional, low-sample-size datasets and outperforming traditional methods.

Contribution

The paper presents a novel neural network feature selection approach with normalization constraints, enhancing feature sparsity and selection accuracy in challenging high-dimensional data.

Findings

SNEL-FS effectively selects important features.

Outperforms conventional feature selection methods.

Works well on high-dimensional, low sample size data.

Abstract

Feature selection is important step in machine learning since it has shown to improve prediction accuracy while depressing the curse of dimensionality of high dimensional data. The neural networks have experienced tremendous success in solving many nonlinear learning problems. Here, we propose new neural-network based feature selection approach that introduces two constrains, the satisfying of which leads to sparse FS layer. We have performed extensive experiments on synthetic and real world data to evaluate performance of the proposed FS. In experiments we focus on the high dimension, low sample size data since those represent the main challenge for feature selection. The results confirm that proposed Feature Selection Based on Sparse Neural Network Layer with Normalizing Constraints (SNEL-FS) is able to select the important features and yields superior performance compared to other conventional FS methods.