High-dimensional data classification is a challenging problem. A standard approach to tackle this problem is to perform variables selection, e.g. using step-wise or LASSO procedures. Another standard way is to perform dimension reduction, e.g. by  Principal Component Analysis or Partial Least Square procedures. The approach proposed in this paper combines both dimension reduction and variables selection. First, a procedure of clustering of variables is used to built groups of correlated variables in order to reduce the redundancy of information. This dimension reduction step relies on the R package ClustOfVar which can deal with both numerical and categorical variables. Secondly, the most relevant synthetic variables (which are numerical variables summarizing the groups obtained in the first step) are selected with a procedure of variable selection using random forests, implemented in the R package VSURF. Numerical performances of the proposed methodology called CoV/VSURF are compared with direct applications of VSURF or random forests on the original $p$ variables. Improvements obtained with  the CoV/VSURF  procedure are illustrated on two simulated mixed datasets (cases $n > p$ and $n < p$) and on a real proteomic dataset.