Automated design of piecewise-linear classifiers of multiple-class data

Abstract

A method for designing multiple-class piecewise-linear classifiers is described. It involves the cutting or arcs joining pairs of opposed points in d-dimensional space. Such arcs are referred to as links. It is shown how to nearly minimize the number of hyperplanes required to cut all of these links, thereby yielding a near-Bayes-optimal decision surface regardless of the number of classes. The underlying theory is described. This method does not require parameters to be specified by users. Experiments on multiple-class data obtained from ship images show that classifiers designed by this method yield approximately the same error rate as the best k-nearest-neighbor rule, while possessing greater computational efficiency of classification.<>