Lasso based feature selection for malaria risk exposure prediction

Abstract : 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.
Type de document :
Communication dans un congrès
Petra Perner. Machine Learning and Data Mining in Pattern Recognition, Jul 2015, Hamburg, Germany. Ibai publishing, 2015, Machine Learning and Data Mining in Pattern Recognition (proceedings of 11th International Conference, MLDM 2015)
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https://hal.archives-ouvertes.fr/hal-01222403
Contributeur : Fabrice Rossi <>
Soumis le : jeudi 29 octobre 2015 - 19:42:29
Dernière modification le : jeudi 5 novembre 2015 - 01:05:59
Document(s) archivé(s) le : vendredi 28 avril 2017 - 06:08:12

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Distributed under a Creative Commons Paternité - Partage selon les Conditions Initiales 4.0 International License

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  • HAL Id : hal-01222403, version 1
  • ARXIV : 1511.01284

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Bienvenue Kouwayè, Noël Fonton, Fabrice Rossi. Lasso based feature selection for malaria risk exposure prediction. Petra Perner. Machine Learning and Data Mining in Pattern Recognition, Jul 2015, Hamburg, Germany. Ibai publishing, 2015, Machine Learning and Data Mining in Pattern Recognition (proceedings of 11th International Conference, MLDM 2015). <hal-01222403>

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