ROCCER: A ROC convex hull rule learning algorithm
Ronaldo Cristiano Prati, Peter A. Flach · 2004
In this paper we propose a method to construct rule sets that have a convex hull in ROC space. We introduce a rule selection algorithm called ROCCER, which operates by selecting rules from a larger set of rules in order to optimise Area Under the ROC Curve (AUC). Compared with set covering algorithms, our method is less dependent on the previously induced rules. Experimental results on three UCI datasets show significant improvements on two of these.