Gene feature selection

This chapter presents an overview on the classes of methods available for feature selection, paying special attention to the problems typical to microarray data processing, where the number of measured genes (factors) is extremely large, in the order of thousands, and the number of relevant factors is much smaller. The main ingredients needed in the selection of an optimal feature set consist in: the search procedures, the underlying optimality criteria, and the procedures for performance evaluation. We discuss here some of the major classes of procedures which are apparently very different in nature and goals: a typical Bayesian framework, several deterministic settings and finally information theoretic methods. Due to space constraints only the major issues are followed, with the intent to clarify the basic principles and the main options when choosing one of the many existing feature selection methods.