A classifier for quantitative feature values based on a region oriented symbolic approach

Abstract

In this paper, a classifier for quantitative feature values (intervals and/or points) based on a region oriented symbolic approach is proposed. In the learning step, each class is described by a region (or a set of regions) in ℛp defined by a convex hull. To affect a new object to a class a dissimilarity matching function compares the class description (a region or a set of regions) with a point in ℛp. Experiments with two artificial data sets generated according to bi-variate normal distributions have been performed in order to show the usefulness of this classifier. The evaluation of the proposed classifier is accomplished according to the calculation of the prediction accuracy (error rate), speed and storage measurements computed through of a Monte Carlo simulation method with 100 replications. © Springer-Verlag Berlin Heidelberg 2004.