Neural Network-Based Approach for Supervised Nonlinear Feature Selection

Mamadou Kanouté, Edith Grall-Maës, Pierre Beauseroy

2023

Abstract

In machine learning, the complexity of training a model increases with the size of the considered feature space. To overcome this issue, feature or variable selection methods can be used for selecting a subset of relevant variables. In this paper we start from an approach initially proposed for classification problems based on a neural network with one hidden layer in which a regularization term is incorporated for variable selection and then show its effectiveness for regression problems. As a contribution, we propose an extension of this approach in the multi-output regression framework. Experiments on synthetic data and real data show the effectiveness of this approach in the supervised framework and compared to some methods of the literature.

Paper Citation

in Harvard Style

Kanouté M., Grall-Maës E. and Beauseroy P. (2023). Neural Network-Based Approach for Supervised Nonlinear Feature Selection. In Proceedings of the 15th International Joint Conference on Computational Intelligence - Volume 1: NCTA; ISBN 978-989-758-674-3, SciTePress, pages 431-439. DOI: 10.5220/0012185700003595

in Bibtex Style

@conference{ncta23,
author={Mamadou Kanouté and Edith Grall-Maës and Pierre Beauseroy},
title={Neural Network-Based Approach for Supervised Nonlinear Feature Selection},
booktitle={Proceedings of the 15th International Joint Conference on Computational Intelligence - Volume 1: NCTA},
year={2023},
pages={431-439},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012185700003595},
isbn={978-989-758-674-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computational Intelligence - Volume 1: NCTA
TI - Neural Network-Based Approach for Supervised Nonlinear Feature Selection
SN - 978-989-758-674-3
AU - Kanouté M.
AU - Grall-Maës E.
AU - Beauseroy P.
PY - 2023
SP - 431
EP - 439
DO - 10.5220/0012185700003595
PB - SciTePress