A fast metric approach to feature subset selection

A simple approach to feature subset selection is proposed. During the training stage, the method selects the features that simultaneously minimize the within-class distance and maximize the between-class distance. Experiments performed on the Iris Plants Database and the Pima Indians Diabetes Database show that the approach is practical because it is fast and yet the correct classification rates are competitive.