The All Relevant Feature Selection using Random Forest
  In this paper we examine the application of the random forest classifier for
the all relevant feature selection problem. To this end we first examine two
recently proposed all relevant feature selection algorithms, both being a
random forest wrappers, on a series of synthetic data sets with varying size.
We show that reasonable accuracy of predictions can be achieved and that
heuristic algorithms that were designed to handle the all relevant problem,
have performance that is close to that of the reference ideal algorithm. Then,
we apply one of the algorithms to four families of semi-synthetic data sets to
assess how the properties of particular data set influence results of feature
selection. Finally we test the procedure using a well-known gene expression
data set. The relevance of nearly all previously established important genes
was confirmed, moreover the relevance of several new ones is discovered.
