Experimental Evaluation of Discretization Schemes for Rule Induction

This paper proposes an experimental evaluation of various discretization schemes in three different evolutionary systems for inductive concept learning. The various discretization methods are used in order to obtain a number of discretization intervals, which represent the basis for the methods adopted by the systems for dealing with numerical values. Basically, for each rule and attribute, one or many intervals are evolved, by means of ad–hoc operators. These operators, depending on the system, can add/subtract intervals found by a discretization method to/from the intervals described by the rule, or split/merge these intervals. In this way the discretization intervals are evolved along with the rules. The aim of this experimental evaluation is to determine for an evolutionary–based system the discretization method that allows the system to obtain the best results. Moreover we want to verify if there is a discretization scheme that can be considered as generally good for evolutionary–based systems. If such a discretization method exists, it could be adopted by all the systems for inductive concept learning using a similar strategy for dealing with numerical values. Otherwise, it would be interesting to extract relationships between the performance of a system and the discretizer used.