Machine Learning
Computational geometry: algorithms and applications
Computational geometry: algorithms and applications
Prototype selection for the nearest neighbour rule through proximity graphs
Pattern Recognition Letters
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Corrections to "Pareto-based multiobjective machine learning: An overview and case studies"
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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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.