most citedLasso based feature selection for malaria risk exposure prediction

4 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

q-bio.PE2016★ 1 cited

Modeling the Influence of Local Environmental Factors on Malaria Transmission in Benin and Its Implications for Cohort Study

Gilles Cottrell, Bienvenue Kouwaye, Charlotte Pierrat +7

Malaria remains endemic in tropical areas, especially in Africa. For the evaluation of new tools and to further ourunderstanding of host-parasite interactions, knowing the environm…

stat.AP2016

Anopheles number prediction on environmental and climate variables using Lasso and stratified two levels cross validation

Bienvenue Kouwaye

This paper deals with prediction of anopheles number using environmental and climate variables. The variables selection is performed by an automatic machine learning method based o…

stat.ML2016★ 1 cited

Regression Trees and Random forest based feature selection for malaria risk exposure prediction

Bienvenue Kouwayè

This paper deals with prediction of anopheles number, the main vector of malaria risk, using environmental and climate variables. The variables selection is based on an automatic m…

stat.ML2015★ 4 cited

Lasso based feature selection for malaria risk exposure prediction

Bienvenue Kouwayè, Noël Fonton, Fabrice Rossi

In life sciences, the experts generally use empirical knowledge to recode variables, choose interactions and perform selection by classical approach. The aim of this work is to per…

stat.ML2015★ 1 cited

Sélection de variables par le GLM-Lasso pour la prédiction du risque palustre

Bienvenue Kouwayè, Noël Fonton, Fabrice Rossi

In this study, we propose an automatic learning method for variables selection based on Lasso in epidemiology context. One of the aim of this approach is to overcome the pretreatme…