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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Communication dans un congrès
W. Duivesteijn, A. Siebes, A. Ukkonen (eds.). 17th International Symposium on Intelligent Data Analysis, IDA2018, Oct 2018, 's-Hertogenbosch, Netherlands. Springer, 11191, pp.290-302, Proceedings of the 17th International Symposium on Intelligent Data Analysis, IDA2018, Lecture Notes in Computer Science
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https://hal.archives-ouvertes.fr/hal-01986660
Contributeur : Christine Sinoquet <>
Soumis le : samedi 19 janvier 2019 - 00:01:36
Dernière modification le : lundi 21 janvier 2019 - 15:09:51

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  • HAL Id : hal-01986660, version 1

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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.. W. Duivesteijn, A. Siebes, A. Ukkonen (eds.). 17th International Symposium on Intelligent Data Analysis, IDA2018, Oct 2018, 's-Hertogenbosch, Netherlands. Springer, 11191, pp.290-302, Proceedings of the 17th International Symposium on Intelligent Data Analysis, IDA2018, Lecture Notes in Computer Science. 〈hal-01986660〉

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