  Many machine learning methods can produce variable importance scores
expressing the usability of each feature in context of the produced model;
those scores on their own are yet not sufficient to generate feature selection,
especially when an all relevant selection is required. Although there are
wrapper methods aiming to solve this problem, they introduce a substantial
increase in the required computational effort.
  In this paper I investigate an idea of incorporating all relevant selection
within the training process by producing importance for implicitly generated
shadows, attributes irrelevant by design. I propose and evaluate such a method
in context of random ferns classifier. Experiment results confirm the
effectiveness of such approach, although show that fully stochastic nature of
random ferns limits its applicability either to small dimensions or as a part
of a broader feature selection procedure.
