A supervised discretization method for quantitative and qualitative ordered variables

In this work, a new technique to define cut-points in the discretization process of a continuous attribute is presented. This method is used as a prior step in a regression problem, considered as a learning problem in which the output variable can be either quantitative (continuous or discreet) or qualitative defined over an ordinal scale. The proposed method emphasizes the concept of location to determine discretization cut-points. In the case of continuous outputs, the method is based on the maximization of the difference between distributions by using intervalar distances. In the case of qualitative outputs, a qualitative distance is defined over a structure of absolute orders of magnitude. The main characteristics of the method presented are illustrated through three examples, two for continuous outputs and the last for a qualitative output.