Multi-Objective Semi-Supervised Feature Selection and Model Selection Based on Pearson's Correlation Coefficient

This paper presents a Semi-Supervised Feature Selection Method based on a univariate relevance measure applied to a multiobjective approach of the problem. Along the process of decision of the optimal solution within Pareto-optimal set, atempting to maximize the relevance indexes of each feature, it is possible to determine a minimum set of relevant features and, at the same time, to determine the optimal model of the neural network.

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