F.: Experimental evaluation of discretization schemes for rule induction

Abstract. This paper proposes an experimental evaluation of various discretization schemes in three dierent evolutionary systems for induc-tive 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 numeri-cal values. Basically, for each rule and attribute, one or many intervals are evolved, by means of ad{hoc operators. These operators, depend-ing 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 us-ing 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.