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Neural Network-Based Approach for Supervised Nonlinear Feature Selection

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, Multi-Output Regression, Supervised Nonlinear Feature Selection.

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.

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Paper citation in several formats:
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 - NCTA; ISBN 978-989-758-674-3; ISSN 2184-3236, SciTePress, pages 431-439. DOI: 10.5220/0012185700003595

@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 - NCTA},
year={2023},
pages={431-439},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012185700003595},
isbn={978-989-758-674-3},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computational Intelligence - NCTA
TI - Neural Network-Based Approach for Supervised Nonlinear Feature Selection
SN - 978-989-758-674-3
IS - 2184-3236
AU - Kanouté, M.
AU - Grall-Maës, E.
AU - Beauseroy, P.
PY - 2023
SP - 431
EP - 439
DO - 10.5220/0012185700003595
PB - SciTePress

- Science and Technology Publications, Lda.
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