Classification of imprecise data using interval Fisher discriminator

Abstract

In this paper, an imprecise data classification is considered using new version of Fisher discriminator, namely interval Fisher. In the conventional formulation of Fisher, elements of within-class scatter matrix (related to covariance matrix between clusters) and between-class scatter matrix (related to covariance matrix of centers of clusters) have single values; but in the interval Fisher, the elements of matrices are in the interval form and can vary in a range. The particle swarm optimization search method is used for solving a constrained optimization problem of the interval Fisher discriminator. Unlike conventional Fisher with one optimal hyperplane, interval Fisher gives two optimal hyperplanes thereupon three decision regions are obtained. Two classes with regard to imprecise scatter matrices are derived by decision making using these optimal hyperplanes. Also, fuzzy region lets us help in fuzzy decision over input test samples. Unlike a support vector classifier with two parallel hyperplanes, interval Fisher generally gives us two nonparallel hyperplanes. Experimental results show the suitability of this idea. © 2011 Wiley Periodicals, Inc.