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Regular version of the site

Statistical analysis of experiments in data mining and computational intelligence (Salvador García)

The interest in nonparametric statistical analysis has grown recently in the field of data mining and computational intelligence. In many experimental studies, the lack of the required properties for a proper application of parametric procedures - independence, normality and homoscedasticity - yields to nonparametric ones the task of performing a rigorous comparison among algorithms.

In this tutorial, we will discuss the basics and give a survey of a complete set of nonparametric procedures developed to perform both pairwise and multiple comparisons, for multiple-problem analysis. We present some studies involving a set of techniques in different topics which can be used for doing a rigorous comparison among the algorithms studied in an experimental comparison. This tutorial is concluded with a compilation of considerations and recommendations, which will guide practitioners when using these tests to contrast their experimental results.