Random forest framework customized to handle highly correlated variables: an extensive experimental study applied to feature selection in genetic data.

Christine Sinoquet; Kamel Mekhnacha

doi:10.1109/dsaa.2018.00032

Communication Dans Un Congrès Année : 2018

Random forest framework customized to handle highly correlated variables: an extensive experimental study applied to feature selection in genetic data.

(1, 2) , (3)

1
2
3

Christine Sinoquet

Fonction : Auteur
PersonId : 171562
IdHAL : christine-sinoquet
IdRef : 033239029

Laboratoire des Sciences du Numérique de Nantes

Data User Knowledge

CV

Kamel Mekhnacha

Fonction : Auteur

Probayes [Montbonnot]

Domaines

Informatique [cs] Intelligence artificielle [cs.AI] Apprentissage [cs.LG]

Christine Sinoquet : Connectez-vous pour contacter le contributeur

https://hal.science/hal-01986653

Soumis le : vendredi 18 janvier 2019-23:40:11

Dernière modification le : mardi 27 août 2024-12:49:52

Dates et versions

hal-01986653 , version 1 (18-01-2019)

Identifiants

HAL Id : hal-01986653 , version 1
DOI : 10.1109/dsaa.2018.00032

Citer

Christine Sinoquet, Kamel Mekhnacha. Random forest framework customized to handle highly correlated variables: an extensive experimental study applied to feature selection in genetic data.. IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA2018, Oct 2018, Turin, Italy. pp.217-226, ⟨10.1109/dsaa.2018.00032⟩. ⟨hal-01986653⟩

Exporter

BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite

Collections

UNIV-NANTES CNRS EC-NANTES UNAM LS2N LS2N-DUKE NANTES-UNIVERSITE

69 Consultations

0 Téléchargements

Random forest framework customized to handle highly correlated variables: an extensive experimental study applied to feature selection in genetic data.

Domaines

Dates et versions

Identifiants

Citer

Exporter

Collections

Altmetric

Partager