Unsupervised Feature Selection Using Extreme Learning Machine

Mamadou Kanouté, Edith Grall-Maës, Pierre Beauseroy

2024

Abstract

In machine learning, feature selection is an important step in building an inference model with good generalization capacity when the number of variables is large. It can be supervised when the goal is to select features with respect to one or several target variables or unsupervised where no target variable is considered and the goal is to reduce the number of variables by removing redundant variables or noise. In this paper, we propose an unsupervised feature selection approach based on a model that uses a neural network with a single hidden layer in which a regularization term is incorporated to deal with nonlinear feature selection for multi-target regression problems. Experiments on synthetic and real-world data and comparisons with some methods in the literature show the effectiveness of this approach in the unsupervised framework.

Paper Citation

in Harvard Style

Kanouté M., Grall-Maës E. and Beauseroy P. (2024). Unsupervised Feature Selection Using Extreme Learning Machine. In Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA; ISBN 978-989-758-721-4, SciTePress, pages 621-628. DOI: 10.5220/0013067500003837

in Bibtex Style

@conference{ncta24,
author={Mamadou Kanouté and Edith Grall-Maës and Pierre Beauseroy},
title={Unsupervised Feature Selection Using Extreme Learning Machine},
booktitle={Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA},
year={2024},
pages={621-628},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013067500003837},
isbn={978-989-758-721-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA
TI - Unsupervised Feature Selection Using Extreme Learning Machine
SN - 978-989-758-721-4
AU - Kanouté M.
AU - Grall-Maës E.
AU - Beauseroy P.
PY - 2024
SP - 621
EP - 628
DO - 10.5220/0013067500003837
PB - SciTePress