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

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Communication dans un congrès
F. Bonchi, F. Provost, T. Eliassi-Rad, W. Wang, C. Cattuto, R. Ghani (eds.). IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA2018, Oct 2018, Turin, Italy. pp.217-226, Proceedings of the IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA2018
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https://hal.archives-ouvertes.fr/hal-01986653
Contributeur : Christine Sinoquet <>
Soumis le : vendredi 18 janvier 2019 - 23:40:11
Dernière modification le : lundi 21 janvier 2019 - 15:10:18

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  • HAL Id : hal-01986653, version 1

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Christine Sinoquet, Kamel Mekhnacha. Random forest framework customized to handle highly correlated variables: an extensive experimental study applied to feature selection in genetic data.. F. Bonchi, F. Provost, T. Eliassi-Rad, W. Wang, C. Cattuto, R. Ghani (eds.). IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA2018, Oct 2018, Turin, Italy. pp.217-226, Proceedings of the IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA2018. 〈hal-01986653〉

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