Optimization of the AUC criterion for rule subset selection

The area under the ROC curve (AUC) is considered a relevant criterion to deal with imbalanced data, misclassification costs and noisy data. Based on this preference, we present an algorithm for rule subset selection. The algorithm builds a Pareto Front using the Sensitivity and Specificity criteria selecting rules from a large set of rules. An empirical study is carried out to verify the influence of the Apriori Parameter in Pareto Front Elite Algorithm. We compare our results with other rule induction algorithms and the results show that the new algorithm obtains a set of rules with greater values of the AUC.