Lasso based feature selection for malaria risk exposure prediction

11/04/2015
by   Bienvenue Kouwayè, et al.
0

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 perform automatic learning algorithm for variables selection which can lead to know if experts can be help in they decision or simply replaced by the machine and improve they knowledge and results. The Lasso method can detect the optimal subset of variables for estimation and prediction under some conditions. In this paper, we propose a novel approach which uses automatically all variables available and all interactions. By a double cross-validation combine with Lasso, we select a best subset of variables and with GLM through a simple cross-validation perform predictions. The algorithm assures the stability and the the consistency of estimators.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/09/2015

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

In this study, we propose an automatic learning method for variables sel...
research
06/24/2016

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

This paper deals with prediction of anopheles number, the main vector of...
research
06/01/2020

A Combined Approach To Detect Key Variables In Thick Data Analytics

In machine learning one of the strategic tasks is the selection of only ...
research
08/09/2018

On feature selection and evaluation of transportation mode prediction strategies

Transportation modes prediction is a fundamental task for decision makin...
research
05/04/2018

Hedging parameter selection for basis pursuit

In Compressed Sensing and high dimensional estimation, signal recovery o...
research
11/03/2020

Automated Hyperparameter Selection for the PC Algorithm

The PC algorithm infers causal relations using conditional independence ...
research
03/10/2020

Short-Term Forecasting of CO2 Emission Intensity in Power Grids by Machine Learning

A machine learning algorithm is developed to forecast the CO2 emission i...

Please sign up or login with your details

Forgot password? Click here to reset