Hybrid Improved Physarum Learner for Structure Causal Discovery

Joao Soares; Vitor Barth; Alan Eckeli; Carlos Maciel

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Paper

Hybrid Improved Physarum Learner for Structure Causal Discovery

Topics: Medical Signal Acquisition, Analysis and Processing; Neural Networks for Biosignal Data; Pattern Recognition & Machine Learning for Biosignal Data

In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSTEC, 234-240, 2023 , Lisbon, Portugal

Authors: Joao Soares ¹ ; Vitor Barth ¹ ; Alan Eckeli ² and Carlos Maciel ¹

Affiliations: ¹ Department of Electrical and Computing Engineering, University of Sao Paulo, Sao Carlos, Brazil ; ² Hospital das Clínicas da Faculdade de Medicina, Ribeirão Preto, Brazil

Keyword(s): Bayesian Networks, Physarum Learner, PC Algorithm, Structure Learning.

Abstract: Causal discovery is the problem of estimating a joint distribution from observational data. In recent years, hybrid algorithms have been proposed to overcome computational problems that lead to better results. This work presents a hybrid approach that combines PC algorithm independence tests with a bio-inspired Improved Physarum Learner algorithm. The combination indicates improvement in computational time spent and yet consistent structural results.

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Paper citation in several formats:

Soares, J.; Barth, V.; Eckeli, A. and Maciel, C. (2023). Hybrid Improved Physarum Learner for Structure Causal Discovery. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - BIOSIGNALS; ISBN 978-989-758-631-6; ISSN 2184-4305, SciTePress, pages 234-240. DOI: 10.5220/0011671000003414

@conference{biosignals23,
author={Joao Soares. and Vitor Barth. and Alan Eckeli. and Carlos Maciel.},
title={Hybrid Improved Physarum Learner for Structure Causal Discovery},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - BIOSIGNALS},
year={2023},
pages={234-240},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011671000003414},
isbn={978-989-758-631-6},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - BIOSIGNALS
TI - Hybrid Improved Physarum Learner for Structure Causal Discovery
SN - 978-989-758-631-6
IS - 2184-4305
AU - Soares, J.
AU - Barth, V.
AU - Eckeli, A.
AU - Maciel, C.
PY - 2023
SP - 234
EP - 240
DO - 10.5220/0011671000003414
PB - SciTePress