Multi-objective neural network model selection with a graph-based large margin approach.

Abstract

This work presents a new decision-making strategy for multi-objective learning problem of artificial neural networks (ANN). The proposed decision-maker searches for the solution that minimizes a margin-based validation error amongst Pareto set solutions. The proposal is based on a geometric approximation to find the large margin (distance) of separation among the classes. Several benchmarks commonly available in the literature were used for testing. The obtained results showed that the proposal is more efficient in controlling the generalization capacity of neural models than other learning machines. It yields smooth (noise robustness) and well-fitted models straightforwardly, i.e., without the necessity of parameter set definition in advance or validation data use, as often required by learning machines.