FEATURE-SELECTION BASED ON THE STRUCTURAL INDEXES OF CATEGORIES

A new technique is proposed to select features out of all available ones on the basis of structural indices of categories. In terms of hyper-rectangles including as many training samples of a category as possible, two characteristic indices are calculated which summarize its underlying distribution of samples. The hyper-rectangles and the indices are available in evaluating the degree of importance of features, and are used to increase the discrimination rates of discrimination rules by removing redundant features. The running time of the algorithm is linear order in the number of features. Experiments on artificial and real data attests its effectiveness.