Current feature selection techniques in statistical pattern recognition

The paper addresses the problem of feature selection (abbreviated FS in the sequel) in statistical pattern recognition with particular emphasis to recent knowledge. Besides over-viewing advances in methodology it attempts to put them into a taxonomical framework. The methods discussed include the latest variants of the Branch & Bound algorithm, enhanced sub-optimal techniques and the simultaneous semi-parametric probability density function modeling and feature space selection method.