The impact of rule ranking on the quality of associative classifiers

Abstract

Associative classification is a promising approach that utilises association rule mining to build classifiers. Associative classification techniques such as CBA and CMAR rank rules mainly in terms of their confidence, support and cardinality. We propose a rule sorting method that adds more tie breaking conditions than existing methods in order to reduce rule random selection. In particular, our method looks at the class distribution frequency associated with the tied rules and favours those that are associated with the majority class. We compare the impact of the proposed rule ranking method and two other methods presented in associative classification against 12 highly dense classification data sets. Our results indicate the effectiveness of the proposed rule ranking method on the quality of the resulting classifiers for the majority of the benchmark problems, which we consider. In particular, our method improved the accuracy on average +0.62% and +0.40% for the 12 benchmark problems if compared with (support, confidence) and (support, confidence, lower cardinality) rule ranking approaches, respectively. This provides evidence that adding more appropriate constraints to break ties between rules positively affects the predictive power of the resulting associative classifiers. © 2006 Springer-Verlag London.