Impact of Recursive Feature Elimination with Cross-validation in Modeling the Spatial Distribution of Three Mosquito Species in Morocco
2022
Douider, Meriem | Amrani, Ibrahim | Balenghien, Thomas | Bennouna, Amal | Abik, Mounia | Ecole Nationale Supérieure d'Informatique et d'Analyses des Systèmes (ENSIAS) ; Université Mohammed V de Rabat [Agdal] (UM5) | Animal, Santé, Territoires, Risques et Ecosystèmes (UMR ASTRE) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Département Systèmes Biologiques (Cirad-BIOS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad) | Institut Agronomique et Vétérinaire Hassan II (IAV Hassan II) | Découverte de pathogènes – Pathogen discovery ; Institut Pasteur [Paris] (IP)-Université Paris Cité (UPCité) | This work is supported by the National Center for Scientific and Technical Research (CNRST), Morocco.
International audience
Afficher plus [+] Moins [-]anglais. Many studies in ecology are interested in characterizing the ecological factors; determining the distribution of animal species. The classical approach consists in identifying the combination of ecological factors that allow reproducing observations of the presence and absence of the species of interest. The major difficulty lies in the imbalance between a considerable quantity of ecological factors to be tested and a relatively limited number of presence/absence observations. Selection of the most influential ecological features is a classical data pre-processing strategy that aims to overcome this imbalance and improve model performance. In this paper, we applied recursive feature elimination with cross-validation (RFECV) approach on presence/absence mosquito data in Morocco; to select optimal subsets of ecological features, in order to improve the performance of the predictive models. This method demonstrated the best ability to improve the performance of the predictive models, and can be recommended as a modeling improvement technique for large datasets.
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