Evolutive Approaches for Variable Selection Using a Non-parametric Noise Estimator

The design of a model to approximate a function relies significantly on the data used in the training stage. The problem of selecting an adequate set of variables should be treated carefully due to its importance. If the number of variables is high, the number of samples needed to design the model becomes too large and the interpretability of the model is lost. This chapter presents several methodologies to perform variable selection in a local or a globalmanner using a non-parametric noise estimator to determine the quality of a subset of variables. Several methods that apply parallel paradigms in different architecures are compared from the optimization and efficiency point of view since the problem is computationally expensive.