Adapting the CBA algorithm by means of intensity of implication.

In recent years, extensive research has been carried out by using association rules to build more accurate classifiers. The idea behind these integrated approaches is to focus on a limited subset of association rules. This paper aims to contribute to this integrated framework by adapting the Classification Based on Associations (CBA) 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 financial 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. [ABSTRACT FROM AUTHOR]