Fast computation of generalized characteristic rules in the cascade model

Abstract

The cascade model is a system to derive characteristic rules, in which a rule condition is expressed by "IF main_condition ADDED ON preconditions" and its consequent part is expressed by a distribution changes in the class attribute. The rule strength is shown by the BSS (between_groups sum of squares) calculated from the class distributions before and after the application of main condition. This paper proposes a generalization of the rule by the incorporation of conjunctive conditions into the main condition part. A fast and exhaustive method to enumerate all candidate rules is also implemented using the FP-Tree algorithm. Two rule selection schemes are introduced to decrease the number of rules. Experimental results on a few representative dataset are reported.