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. © 2010 Springer-Verlag.
CITATION STYLE
Coelho, F., Braga, A. P., & Verleysen, M. (2010). Multi-objective semi-supervised feature selection and model selection based on Pearson’s correlation coefficient. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6419 LNCS, pp. 509–516). https://doi.org/10.1007/978-3-642-16687-7_67
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