On Feature Selection Using Anisotropic General Regression Neural Network

TL;DR

This paper introduces a feature selection method using an anisotropic General Regression Neural Network with Gaussian kernels, demonstrating its robustness and effectiveness through experiments on simulated and real datasets.

Contribution

It presents a novel feature selection approach leveraging anisotropic GPRNN, improving interpretability and predictive quality in machine learning models.

Findings

The method is robust to sample size variations.

It outperforms four existing feature selection methods.

Effective on both simulated and real datasets.

Abstract

The presence of irrelevant features in the input dataset tends to reduce the interpretability and predictive quality of machine learning models. Therefore, the development of feature selection methods to recognize irrelevant features is a crucial topic in machine learning. Here we show how the General Regression Neural Network used with an anisotropic Gaussian Kernel can be used to perform feature selection. A number of numerical experiments are conducted using simulated data to study the robustness of the proposed methodology and its sensitivity to sample size. Finally, a comparison with four other feature selection methods is performed on several real world datasets.