Abstract
ROC curves have been used for a fair comparison of machine learning algorithms since the late 90's. Accordingly, the area under the ROC curve (AUC) is nowadays considered a relevant learning criterion, accommodating imbalanced data, misclassification costs and noisy data. This paper shows how a genetic algorithm-based optimization of the AUC criterion can be exploited for impact studies and sensitivity analysis. The approach is illustrated on the Atherosclerosis Identification problem, PKDD 2002 Challenge.