EasyFS: an Efficient Model-free Feature Selection Framework via Elastic Transformation of Features

TL;DR

EasyFS is a fast, model-free feature selection method that uses elastic transformation and non-linear projections to better capture feature interrelationships, outperforming existing methods in accuracy and efficiency.

Contribution

The paper introduces EasyFS, a novel feature selection framework that models feature interdependencies through elastic expansion and compression, achieving superior performance without relying on model-based approaches.

Findings

Outperforms state-of-the-art methods by up to 10.9% in regression.

Achieves up to 5.7% improvement in classification accuracy.

Reduces computational time by over 94%.

Abstract

Traditional model-free feature selection methods treat each feature independently while disregarding the interrelationships among features, which leads to relatively poor performance compared with the model-aware methods. To address this challenge, we propose an efficient model-free feature selection framework via elastic expansion and compression of the features, namely EasyFS, to achieve better performance than state-of-the-art model-aware methods while sharing the characters of efficiency and flexibility with the existing model-free methods. In particular, EasyFS expands the feature space by using the random non-linear projection network to achieve the non-linear combinations of the original features, so as to model the interrelationships among the features and discover most correlated features. Meanwhile, a novel redundancy measurement based on the change of coding rate is proposed for efficient filtering of redundant features. Comprehensive experiments on 21 different datasets show that EasyFS outperforms state-of-the art methods up to 10.9\% in the regression tasks and 5.7\% in the classification tasks while saving more than 94\% of the time.