  The main purpose of Feature Subset Selection is to find a reduced subset of
attributes from a data set described by a feature set. The task of a feature
selection algorithm (FSA) is to provide with a computational solution motivated
by a certain definition of relevance or by a reliable evaluation measure. In
this paper several fundamental algorithms are studied to assess their
performance in a controlled experimental scenario. A measure to evaluate FSAs
is devised that computes the degree of matching between the output given by a
FSA and the known optimal solutions. An extensive experimental study on
synthetic problems is carried out to assess the behaviour of the algorithms in
terms of solution accuracy and size as a function of the relevance,
irrelevance, redundancy and size of the data samples. The controlled
experimental conditions facilitate the derivation of better-supported and
meaningful conclusions.
