  Feature selection (FS) is a process which attempts to select more informative
features. In some cases, too many redundant or irrelevant features may
overpower main features for classification. Feature selection can remedy this
problem and therefore improve the prediction accuracy and reduce the
computational overhead of classification algorithms. The main aim of feature
selection is to determine a minimal feature subset from a problem domain while
retaining a suitably high accuracy in representing the original features. In
this paper, Principal Component Analysis (PCA), Rough PCA, Unsupervised Quick
Reduct (USQR) algorithm and Empirical Distribution Ranking (EDR) approaches are
applied to discover discriminative features that will be the most adequate ones
for classification. Efficiency of the approaches is evaluated using standard
classification metrics.
