Deep Feature Selection Using a Novel Complementary Feature Mask

TL;DR

This paper introduces a novel complementary feature mask for deep feature selection, which considers less important features to improve selection quality and can be integrated into existing methods.

Contribution

The work proposes a new framework that leverages less important features through a complementary mask, enhancing deep feature selection performance.

Findings

Selects more representative features than existing methods

Improves interpretability by considering less important features

Demonstrates effectiveness on benchmark datasets

Abstract

Feature selection has drawn much attention over the last decades in machine learning because it can reduce data dimensionality while maintaining the original physical meaning of features, which enables better interpretability than feature extraction. However, most existing feature selection approaches, especially deep-learning-based, often focus on the features with great importance scores only but neglect those with less importance scores during training as well as the order of important candidate features. This can be risky since some important and relevant features might be unfortunately ignored during training, leading to suboptimal solutions or misleading selections. In our work, we deal with feature selection by exploiting the features with less importance scores and propose a feature selection framework based on a novel complementary feature mask. Our method is generic and can be easily integrated into existing deep-learning-based feature selection approaches to improve their performance as well. Experiments have been conducted on benchmarking datasets and shown that the proposed method can select more representative and informative features than the state of the art.