When dealing with high dimensional and low sample size data, feature selection is often needed to help reduce the dimension of the variable space while optimizing the classification task. Few tools exist for selecting variables in such data sets, especially when classes are numerous (2). We have...

The instability in the selection of models is a major concern with data sets containing a large number of covariates. This paper deals with variable selection methodology in the case of high-dimensional problems where the response variable can be right censored. We focuse on new stable variable...