A Computational Geometry Approach for Pareto-Optimal Selection of Neural Networks

  • Luiz C. B. Torres
  • Cristiano L. Castro
  • Antônio P. Braga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7553)

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.

Keywords

decision-making multiobjective machine learning gabriel graph classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Luiz C. B. Torres
    • 1
  • Cristiano L. Castro
    • 1
  • Antônio P. Braga
    • 1
  1. 1.Department of Electronics EngineeringFederal University of Minas GeraisBelo HorizonteBrazil

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