Feature selection based on geometric distance for high-dimensional data

Abstract

A novel feature selection method based on geometric distance is proposed. It utilises both the average distance between classes along with the evenness of these distances to evaluate feature subsets. The feature evaluation and selection process used therein is very easy to understand, because it lends itself to a simple geometrical analysis. Moreover, because the method does not calculate the relevance or redundancy between features, it is faster than other filter methods that use information or statistical dependency concepts. The experiments demonstrate its markedly better classification performance as well as fast computation compared with existing methods.