Random forests with latent variables to foster feature selection in the context of highly correlated variables. Illustration with a bioinformatics application.

Christine Sinoquet; Kamel Mekhnacha

doi:10.1007/978-3-030-01768-2_24

Communication Dans Un Congrès Année : 2018

Random forests with latent variables to foster feature selection in the context of highly correlated variables. Illustration with a bioinformatics application.

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Christine Sinoquet

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PersonId : 171562
IdHAL : christine-sinoquet
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Laboratoire des Sciences du Numérique de Nantes

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Kamel Mekhnacha

Fonction : Auteur

Probayes [Montbonnot]

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

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https://hal.science/hal-01986660

Soumis le : samedi 19 janvier 2019-00:01:36

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

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hal-01986660 , version 1 (19-01-2019)

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HAL Id : hal-01986660 , version 1
DOI : 10.1007/978-3-030-01768-2_24

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Christine Sinoquet, Kamel Mekhnacha. Random forests with latent variables to foster feature selection in the context of highly correlated variables. Illustration with a bioinformatics application.. 17th International Symposium on Intelligent Data Analysis, IDA2018, Oct 2018, 's-Hertogenbosch, Netherlands. pp.290-302, ⟨10.1007/978-3-030-01768-2_24⟩. ⟨hal-01986660⟩

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Random forests with latent variables to foster feature selection in the context of highly correlated variables. Illustration with a bioinformatics application.

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