Rule Quality Measures for Rule Induction Systems: Description and Evaluation

Abstract

A rule quality measure is important to a rule induction system for determining when to stop generalization or specialization. Such measures are also important to a rule‐based classification procedure for resolving conflicts among rules. We describe a number of statistical and empirical rule quality formulas and present an experimental comparison of these formulas on a number of standard machine learning datasets. We also present a meta‐learning method for generating a set of formula‐behavior rules from the experimental results which show the relationships between a formula's performance and the characteristics of a dataset. These formula‐behavior rules are combined into formula‐selection rules that can be used in a rule induction system to select a rule quality formula before rule induction. We will report the experimental results showing the effects of formula‐selection on the predictive performance of a rule induction system.