Successfully modeling high-dimensional data involving thousands of variables is challenging. This is especially true for gene expression profiling experiments, given the large number of genes involved and the small number of samples available. Random Forests (RF) is a popular and widely used approach to feature selection for such small n, large p problems. However, Random Forests suffers from instability, especially in the presence of noisy and/or unbalanced inputs.