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Unsupervised Feature Selection Using Extreme Learning Machine

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process; Deep Learning; Neural Approaches and Neural Architectures; Neural based Implementation, Applications and Solutions

Authors: Mamadou Kanouté ; Edith Grall-Maës and Pierre Beauseroy

Affiliation: Computer Science and Digital Society Laboratory (LIST3N), Université de Technologie de Troyes, Troyes, France

Keyword(s): Neural Network, Sparse Learning, Nonlinear Method, Unsupervised Feature Selection.

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.

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Paper citation in several formats:
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 - NCTA; ISBN 978-989-758-721-4; ISSN 2184-3236, SciTePress, pages 621-628. DOI: 10.5220/0013067500003837

@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 - NCTA},
year={2024},
pages={621-628},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013067500003837},
isbn={978-989-758-721-4},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computational Intelligence - NCTA
TI - Unsupervised Feature Selection Using Extreme Learning Machine
SN - 978-989-758-721-4
IS - 2184-3236
AU - Kanouté, M.
AU - Grall-Maës, E.
AU - Beauseroy, P.
PY - 2024
SP - 621
EP - 628
DO - 10.5220/0013067500003837
PB - SciTePress

- Science and Technology Publications, Lda.
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