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-behaviour rules from the experimental results which show the relationships between a formula's performance and the characteristics of a dataset. These formula-behaviour 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.