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ClustOfVar : an R package for dimension reduction via clustering of variables. Application in supervised classification and variable selection in gene expressions data

hal.structure.identifierQuality control and dynamic reliability [CQFD]
dc.contributor.authorCHAVENT, Marie
hal.structure.identifierEquipe de Biostatistique
dc.contributor.authorGENUER, Robin
hal.structure.identifierAménités et dynamiques des espaces ruraux [UR ADBX]
dc.contributor.authorKUENTZ-SIMONET, Vanessa
hal.structure.identifierEquipe de Biostatistique
dc.contributor.authorLIQUET, Benoit
hal.structure.identifierQuality control and dynamic reliability [CQFD]
dc.contributor.authorSARACCO, Jerôme
dc.date.accessioned2024-04-04T02:20:25Z
dc.date.available2024-04-04T02:20:25Z
dc.date.conference2013-01-24
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/189490
dc.description.abstractEnThe main goal of this work is to tackle the problem of dimension reduction for high-dimensional supervised classication. The motivation is to handle gene expression data. The proposed method works in 2 steps. First, one eliminates redundancy using clustering of variables, based on the R-package ClustOfVar. This first step is only based on the exploratory variables (genes). Second, the synthetic variables (summarizing the clusters obtained at the first step) are used to construct a classifier (e.g. logistic regression, LDA, random forests). We stress that the first step reduces the dimension and gives linear combinations of original variables (synthetic variables). This step can be considered as an alternative to PCA. A selection of predictors (synthetic variables) in the second step gives a set of relevant original variables (genes). Numerical performances of the proposed procedure are evaluated on gene expression datasets. We compare our methodology with LASSO and sparse PLS discriminant analysis on these datasets.
dc.language.isoen
dc.title.enClustOfVar : an R package for dimension reduction via clustering of variables. Application in supervised classification and variable selection in gene expressions data
dc.typeCommunication dans un congrès
dc.subject.halStatistiques [stat]/Applications [stat.AP]
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleStatistical Methods for (post)-Genomics Data (SMPGD 2013)
bordeaux.countryNL
bordeaux.peerReviewedoui
hal.identifierhal-00926216
hal.version1
hal.invitednon
hal.proceedingsnon
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00926216v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=CHAVENT,%20Marie&GENUER,%20Robin&KUENTZ-SIMONET,%20Vanessa&LIQUET,%20Benoit&SARACCO,%20Jer%C3%B4me&rft.genre=unknown


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