Multi-Objective Evolutionary Optimization For Generating Ensembles Of Classifiers In The Roc Space

In this paper, we propose a novel approach for the multi-objective 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 of experiments to demonstrate the properties and behaviour of this approach. This allows us to propose interesting venues for future research on optimizing ensembles of classifiers using multi-objective evolutionary algorithms.