Integrating classification and association rules by proposing adaptations to CBA

Abstract

In recent years, extensive research has been carded out by focusing on association rules to build more accurate classifiers. These integrated approaches mainly focus on a limited subset of association rules, i.e. those rules were the consequent of the rule is restricted to the classification class attribute. This paper aims to contribute to this integrated framework by adapting the CBA(Classification Based on Associations)algorithm. CBA was adapted by coupling it with another measurement of the quality of association rules: i.e. intensity of implication. The new algorithm has been implemented and empirically tested on an authentic mancial dataset for purposes of bankruptcy prediction. We validated our results with an association ruleset with C4.5, with original CBA and with CART by statistically comparing its performance via the area under the ROC-curve. The adapted CBA algorithm presented in this paper proved to generate significantly better results than the other classifiers at the 5% level of significance.