Stefano Galelli

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This paper is about dimensionality reduction by variable selection in high–dimensional real–world control problems, where designing controllers by conventional means is either impractical or results in poor performance. In this paper we propose a novel model–free variable selection approach that, starting from a dataset of one–step state transitions and(More)
An emerging trend in feature selection is the development of two-objective algorithms that analyze the tradeoff between the number of features and the classification performance of the model built with these features. Since these two objectives are conflicting, a typical result stands in a set of Pareto-efficient subsets, each having a different cardinality(More)
Abstract: Input variable selection is an essential step in the development of statistical models and is particularly relevant in environmental modelling, where potential model inputs often consist of time lagged values of each different potential input variable. While new methods for identifying important model inputs continue to emerge, each has its own(More)