Feature selection based on information theory, consistency and separability indices

Two new feature selection methods are introduced, the first based on separability criterion, the second on a consistency index that includes interactions between the selected subsets of features. Comparison of accuracy was made against information-theory based selection methods on several datasets training neurofuzzy and nearest neighbor methods on various subsets of selected features. Methods based on separability seem to be most promising.