Building of High-Performance Classifier by Rule Selection from Cascade Model

Abstract

The characteristic rule induction usually produces a large number of rules, and it is difficult for a user to inspect all rules. This paper describes a method to give a priority index to rules based on their supporting instances, and it guides a user to inspect the most useful rule successively. The priority index is calculated dynamically at each step of rule set inspection using the covered instances by the employed rules, and the resulting rule group obtained gives a concise understanding to the data of the target class.