Discretization oriented to Decision Rules Generation
Raudel Giráldez Reyes, Jesús S. Aguilar–Ruiz, J. C. Riquelme, Francisco J. Ferrer-Troyano, Domingo S. Rodríguez–Baena · 2001
Abstract. Many of the supervised learning algorithms only work with spaces of dis-crete attributes. Some of the methods proposed in the bibliography focus on the dis-cretization towards the generation of decision rules. This work provides a new dis-cretization 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.