The decision tree-based module has been trained through a standard C4.5 algorithm [22], obtaining a tree of 45
levels, with a size of 89. The test of such tree, that is performed on the validation dataset provided a rate of correct
classifications of 75.7%, which means that the decision tree successfully classifies all the inputs with respect to the
variable v.