Toward a Surrogate Marker of Malaria Exposure: Modeling Longitudinal Antibody Measurements under Outbreak Conditions, PLoS ONE, vol.6, issue.7, pp.1-6, 2011. ,
DOI : 10.1371/journal.pone.0021826.t001
Modeling the Influence of Local Environmental Factors on Malaria Transmission in Benin and Its Implications for Cohort Study, PLoS ONE, vol.6, issue.1, 2012. ,
DOI : 10.1371/journal.pone.0028812.t001
URL : https://hal.archives-ouvertes.fr/hal-01352441
Patterns and seasonality of malaria transmission in the forest-savannah transitional zones of Ghana, Malaria Journal, vol.9, issue.1, pp.314-21054895, 2010. ,
DOI : 10.1186/1475-2875-9-314
A Climate-based Distribution Model of Malaria Transmission in Sub-Saharan Africa, Parasitology Today, vol.15, issue.3, pp.105-111, 1999. ,
DOI : 10.1016/S0169-4758(99)01396-4
Habitat-based modeling of impacts of mosquito larval interventions on entomological inoculation rates, incidence, and prevalence of malaria. The American journal of tropical medicine and hygiene, pp.546-552, 2005. ,
Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society Series B (Methodological), pp.267-288, 1996. ,
DOI : 10.1111/j.1467-9868.2011.00771.x
Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.5, issue.2, pp.301-320, 2005. ,
DOI : 10.1073/pnas.201162998
An introduction to variable and feature selection, Journal of Machine Learning Research, vol.3, pp.1157-1182, 2003. ,
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties, Journal of the American Statistical Association, vol.96, issue.456, pp.1348-1360, 2001. ,
DOI : 10.1198/016214501753382273
URL : http://www.stat.psu.edu/~rli/research/penlike.pdf
THE LASSO METHOD FOR VARIABLE SELECTION IN THE COX MODEL, 4%3C385::AID-SIM380%3E3.0. CO, pp.385-3952, 1997. ,
DOI : 10.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3
Lasso based feature selection for malaria risk exposure prediction, 2015. ,
Variables selection for Generalized linear mixed models by L 1 -Penality estimation, 2011. ,
Variable selection methods in regression: Ignorable problem, outing notable solution, Journal of Targeting, Measurement and Analysis for Marketing, vol.71, issue.1, pp.65-75, 2010. ,
DOI : 10.1080/01621459.1976.10480949
Subset Selection in Regression, p.235, 1990. ,
Structural feature selection for wrapper methods Available from: https, ESANN 2005, 13th European Symposium on Artificial Neural Networks Proceedings; 2005. p, pp.405-410, 2005. ,
Machine learning applications in cancer prognosis and prediction, Computational and Structural Biotechnology Journal, vol.13, pp.8-17, 2015. ,
DOI : 10.1016/j.csbj.2014.11.005
URL : https://doi.org/10.1016/j.csbj.2014.11.005
Using a Machine Learning Approach to Predict Outcomes after Radiosurgery for Cerebral Arteriovenous Malformations, Scientific Reports, vol.63, issue.1, p.12, 2016. ,
DOI : 10.1007/s10994-006-6226-1
Machine Learning for Personalized Medicine: Predicting Primary Myocardial Infarction from Electronic Health Records. Association for the Advancement of Artificial Intelligence: AI MAGAZINE, p.13, 2012. ,
DOI : 10.1609/aimag.v33i4.2438
URL : https://aaai.org/ojs/index.php/aimagazine/article/download/2438/2332
Improving feature selection performance using pairwise pre-evaluation, BMC Bioinformatics, vol.23, issue.2, p.13, 2016. ,
DOI : 10.1016/S0933-3657(01)00082-3
URL : https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-016-1178-3?site=bmcbioinformatics.biomedcentral.com
An Effective Feature Selection Approach Using the Hybrid Filter Wrapper, International Journal of Hybrid Information Technology, vol.9, issue.1, pp.119-128, 2016. ,
DOI : 10.14257/ijhit.2016.9.1.11
Feature selection and validated predictive performance in the domain of Legionella pneumophila: a comparative study, BMC Research Notes, vol.54, issue.10, p.7, 2016. ,
DOI : 10.1016/S0895-4356(01)00341-9
Lasso and elastic-net regularized generalized linear models, 2015. ,
L 1 Penalized Estimation in Cox Proportional Hazards Model, Biometrical Journal, vol.52, pp.70-84, 2010. ,
Malaria infection and disease in an area with pyrethroid-resistant vectors in southern Benin, Malaria Journal, vol.9, issue.1, pp.380-21194470, 2010. ,
DOI : 10.1186/1475-2875-9-380
Malaria infection and disease in an area with pyrethroid-resistant vectors in southern Benin, Malaria Journal, vol.9, issue.1, pp.380-21194470, 2010. ,
DOI : 10.1186/1475-2875-9-380
The Anophelinae of Africa south of the Sahara), Pub South Afr Inst Med Res Johannesburg, 1968. ,
A supplement to the Anophelinae of Africa south of the Sahara (Afrotropical region), Pub South Afr Inst Med Res, 1987. ,
Comparative testing of monoclonal antibodies against Plasmodium falciparum sporozoites for ELISA development, Bull World Health Organ, vol.65, pp.39-45, 1987. ,
Sélection de variables par le GLM-Lasso pour la prédiction du risque palustre, 47èmes Journees de Statistique de la SFdS, 2015. ,
Lasso Based Feature Selection for Malaria Risk Exposure Prediction, 11th International Conference Poster Proceedings, ibai publishing. Machine Learning and Data Mining in Pattern Recognition. Petra Perner, p.2015, 2015. ,
Regularization Paths for Generalized Linear Models via Coordinate Descent, Journal of Statistical Software, vol.33, issue.1, pp.1-22, 2010. ,
DOI : 10.18637/jss.v033.i01
URL : https://doi.org/10.18637/jss.v033.i01
The elements of statistical learning: data mining, inference, and prediction. Springer series in statistics Available from, 2009. ,
Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study, BMC Bioinformatics, vol.15, issue.1, p.12, 2014. ,
DOI : 10.1186/1471-2105-15-291
URL : https://hal.archives-ouvertes.fr/inserm-01097969
Consistency of random forests and other averaging classifiers, The Journal of Machine Learning Research, p.20152033, 2008. ,
URL : https://hal.archives-ouvertes.fr/hal-00355368
Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution, BMC Bioinformatics, vol.8, issue.25, p.17254353, 2007. ,
Variable selection using random forests, Pattern Recognition Letters, vol.31, issue.14, pp.2225-2236, 2010. ,
DOI : 10.1016/j.patrec.2010.03.014
URL : https://hal.archives-ouvertes.fr/hal-00755489
Gene selection and classification of microarray data using random forest, BMC Bioinformatics, vol.7, issue.3, pp.1-13, 2006. ,
An efficient method for gradient-based adaptation of hyper-parameters in SVM models, 2007. ,