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.
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Torres, L.C.B., Castro, C.L., Braga, A.P. (2012). A Computational Geometry Approach for Pareto-Optimal Selection of Neural Networks. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33266-1_13
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DOI: https://doi.org/10.1007/978-3-642-33266-1_13
Publisher Name: Springer, Berlin, Heidelberg
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