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hal.structure.identifierQuality control and dynamic reliability [CQFD]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorCHAVENT, Marie
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
dc.contributor.authorGENUER, Robin
hal.structure.identifierQuality control and dynamic reliability [CQFD]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierEcole Nationale Supérieure de Cognitique [ENSC]
dc.contributor.authorSARACCO, Jerome
dc.date.issued2021-01-11
dc.identifier.issn0361-0918
dc.description.abstractEnStandard approaches to tackle high-dimensional supervised classification often include variable selection and dimension reduction. The proposed methodology combines clustering of variables and feature selection. Hierarchical clustering of variables allows to built groups of correlated variables and summarizes each group by a synthetic variable. Originality is that groups of variables are unknown a priori. Moreover clustering approach deals with both numerical and categorical variables. Among all the possible partitions, the most relevant synthetic variables are selected with a procedure using random forests. Numerical performances are illustrated on simulated and real datasets. Selection of groups of variables provides easier interpretation of results.
dc.language.isoen
dc.publisherTaylor & Francis
dc.subject.enClustering of variables
dc.subject.enRandom forests
dc.subject.enSupervised classification
dc.subject.enVariable selection
dc.title.enCombining clustering of variables and feature selection using random forests
dc.typeArticle de revue
dc.identifier.doi10.1080/03610918.2018.1563145
dc.subject.halMathématiques [math]/Statistiques [math.ST]
dc.identifier.arxiv1608.06740
bordeaux.journalCommunications in Statistics - Simulation and Computation
bordeaux.page426-445
bordeaux.volume50
bordeaux.issue2
bordeaux.peerReviewedoui
hal.identifierhal-02013631
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02013631v1
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