A computational geometry approach for pareto-optimal selection of neural networks

Abstract

This paper presents a Pareto-optimal selection strategy for multiobjective learning that is based on the geometry of the separation margin between classes. The Gabriel Graph, a method borrowed from Computational Geometry, is constructed in order to obtain margin patterns and class borders. From border edges, a target separator is obtained in order to obtain a large margin classifier. The selected model from the generated Pareto-set is the one that is closer to the target separator. The method presents robustness in both synthetic and real benchmark datasets. It is efficient for Pareto-Optimal selection of neural networks and no claim is made that the obtained solution is equivalent to a maximum margin separator. © 2012 Springer-Verlag.