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CoreSelect: A New Approach to Select Landmarks for Dissimilarity Space Embedding

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; Learning Paradigms and Algorithms

Authors: Sylvain Chabanet ; Philippe Thomas and Hind Bril El-Haouzi

Affiliation: Université de Lorraine, CNRS, CRAN, F-88000 Epinal, France

Keyword(s): Machine Learning, Proximity Learning, Surrogate Modeling, Sawmill Simulation.

Abstract: This paper studies an application of indefinite proximity learning to the prediction of baskets of products of logs in the sawmill industry. More precisely, it focuses on the usage of the dissimilarity space embedding framework to generate a set of features representing wood logs. According to this framework, data points are represented by a vector of dissimilarity measures toward a set of representative data points named landmarks. This representation can then be used to train any of the large variety of available ML models requiring structured features. However, this framework raises the problem of selecting these landmarks. A new method is proposed to select these landmarks which is compared with four other methods from the literature. Numerical experiments are run to compare these methods on a dataset from the Canadian sawmill industry. The data representations obtained are used to train random forests and neural networks ensemble models. Results demonstrate that both the Partiti on Around Medoids (PAM) method and the newly proposed CoreSelect methods lead to a small but significant reduction in the mean square error of the predictions. (More)

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Paper citation in several formats:
Chabanet, S. ; Thomas, P. and Bril El-Haouzi, H. (2023). CoreSelect: A New Approach to Select Landmarks for Dissimilarity Space Embedding. In Proceedings of the 15th International Joint Conference on Computational Intelligence - NCTA; ISBN 978-989-758-674-3; ISSN 2184-3236, SciTePress, pages 479-486. DOI: 10.5220/0012163500003595

@conference{ncta23,
author={Sylvain Chabanet and Philippe Thomas and Hind {Bril El{-}Haouzi}},
title={CoreSelect: A New Approach to Select Landmarks for Dissimilarity Space Embedding},
booktitle={Proceedings of the 15th International Joint Conference on Computational Intelligence - NCTA},
year={2023},
pages={479-486},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012163500003595},
isbn={978-989-758-674-3},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computational Intelligence - NCTA
TI - CoreSelect: A New Approach to Select Landmarks for Dissimilarity Space Embedding
SN - 978-989-758-674-3
IS - 2184-3236
AU - Chabanet, S.
AU - Thomas, P.
AU - Bril El-Haouzi, H.
PY - 2023
SP - 479
EP - 486
DO - 10.5220/0012163500003595
PB - SciTePress

- Science and Technology Publications, Lda.
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