Multi-objective evolutionary optimization for generating ensembles of classifiers in the ROC space
Multi-objective evolutionary optimization for generating ensembles of classifiers in the ROC space
Lévesque, Julien Charles and Durand, Audrey and Gagné, Christian and Sabourin, Robert
GECCO’12 – Proceedings of the 14th International Conference on Genetic and Evolutionary Computation 2012
Abstract : 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. © 2012 ACM.
