Discretization oriented to Decision Rules Generation

Many of the supervised learning algorithms only work with spaces of discrete attributes. Some of the methods proposed in the bibliography focus on the discretization towards the generation of decision rules. This work provides a new discretization algorithm called USD (Unparametrized Supervised Discretization), which transforms the infinite space of the values of the continuous attributes in a finite group of intervals with the purpose of using these intervals in the generation of decision rules, in such a way that these rules do not loose accuracy or goodness. Stands out the fact that, contrary to other methods, USD doesn’t need parameterization.