Classification rule induction based on relevant, irredundant attributes and rule expansion

In this paper we focus on the induction of classification rules from examples. Conventional algorithms fail in discovering effective knowledge when the database contains irrelevant information. We present a new rule extraction method, RGT, which tackles this problem by employing only relevant and irredundant attributes. Simplicity of rules is also our major concern. In order to create only simple rules, we estimate the purity of patterns and propose a rule merging and expending procedures. In this paper, we describe the methodology for the RGT algorithm, discuss its properties, and compare it with conventional methods.