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In this paper, we propose a novel approach for the multiobjective optimization of classifier ensembles in the ROC space. We first evolve a pool of simple classifiers with NSGA-II using values of the ROC curves as the optimization objectives. These simple classifiers are then combined at the decision level using the Iterative Boolean Combination method (IBC). This method produces multiple ensembles of classifiers optimized for various operating conditions. We perform a rigorous series ofdoi:10.1145/2330163.2330285 dblp:conf/gecco/LevesqueDGS12 fatcat:apcuvybdzbe5pmbai5th4x7dki