  Standard approaches to tackle high-dimensional supervised classification
problem often include variable selection and dimension reduction procedures.
The novel methodology proposed in this paper combines clustering of variables
and feature selection. More precisely, hierarchical clustering of variables
procedure allows to build groups of correlated variables in order to reduce the
redundancy of information and summarizes each group by a synthetic numerical
variable. Originality is that the groups of variables (and the number of
groups) are unknown a priori. Moreover the clustering approach used can deal
with both numerical and categorical variables (i.e. mixed dataset). Among all
the possible partitions resulting from dendrogram cuts, the most relevant
synthetic variables (i.e. groups of variables) are selected with a variable
selection procedure using random forests. Numerical performances of the
proposed approach are compared with direct applications of random forests and
variable selection using random forests on the original p variables.
Improvements obtained with the proposed methodology are illustrated on two
simulated mixed datasets (cases n>p and n<p, where n is the sample size) and on
a real proteomic dataset. Via the selection of groups of variables (based on
the synthetic variables), interpretability of the results becomes easier.
