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

Marie Chavent 1 Robin Genuer 2 Vanessa Kuentz-Simonet 3 Benoit Liquet 2 Jerôme Saracco 1
1 CQFD - Quality control and dynamic reliability
IMB - Institut de Mathématiques de Bordeaux, Inria Bordeaux - Sud-Ouest
3 UR ADBX
UR ABDX
Abstract : The 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.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-00926216
Contributor : Marie Chavent <>
Submitted on : Thursday, January 9, 2014 - 11:29:31 AM
Last modification on : Friday, April 19, 2019 - 9:40:02 AM

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Marie Chavent, Robin Genuer, Vanessa Kuentz-Simonet, Benoit Liquet, Jerôme Saracco. ClustOfVar : an R package for dimension reduction via clustering of variables. Application in supervised classification and variable selection in gene expressions data. Statistical Methods for (post)-Genomics Data (SMPGD 2013), Jan 2013, Netherlands. ⟨hal-00926216⟩

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