A non-linear classifier for symbolic interval data based on a region oriented approach

Abstract

This paper presents a non-linear classifier based on a region oriented approach for interval data. Each example of the learning set is described by a interval feature vector. Concerning the learning step, each class is described by a region (or a set of regions)ℜp in defined by hypercube of the objects belonging to this class. In the allocation step, the assignment of a new object to a class is based on a suitable L r Minkowski distance between intervals. Experiments with two synthetic interval data sets have been performed in order to show the usefulness of this classifier. The prediction accuracy (error rate) of the proposed classifier is calculated through a Monte Carlo simulation method with 100 replications. © 2009 Springer Berlin Heidelberg.