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Automatic Modeling of Dynamical Interactions Within Marine Ecosystems

Omar Iken 1 Maxime Folschette 2 Tony Ribeiro 3
2 BioComputing
CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
3 MéForBio - Méthodes Formelles pour la Bioinformatique
LS2N - Laboratoire des Sciences du Numérique de Nantes
Abstract : Marine ecology models are used to study and anticipate population variations of plankton and microalgae species. These variations can have an impact on ecological niches, the economy or the climate. Our objective is the automation of the creation of such models. Learning From Interpretation Transition (LFIT) is a framework that aims at learning the dynamics of a system by observing its state transitions. LFIT provides explainable predictions in the form of logical rules. In this paper, we introduce a method that allows to extract an influence graph from a LFIT model. We also propose an heuristic to improve the model against noise in the data.
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Contributor : Maxime Folschette Connect in order to contact the contributor
Submitted on : Thursday, September 16, 2021 - 5:55:32 PM
Last modification on : Wednesday, October 13, 2021 - 3:52:06 PM


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  • HAL Id : hal-03347033, version 1


Omar Iken, Maxime Folschette, Tony Ribeiro. Automatic Modeling of Dynamical Interactions Within Marine Ecosystems. 2021. ⟨hal-03347033⟩



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