Aler, RicardoHandl, JuliaKnowles, Joshua D.2013-07-112013-07-112013-07GECCO'13: Proceedings of the Fifteenth International Conference on Genetic and Evolutionary Computation Conference, Amsterdam, The Netherlands, July 06-10, 2013. New York: ACM, 2013, 1029-1036.https://hdl.handle.net/10016/17280Proceedings of: GECCO 2013: 15th International Conference on Genetic and Evolutionary Computation Conference (Amsterdam, The Netherlands, July 06-10, 2013): a recombination of the 22nd International Conference on Genetic Algorithms (ICGA) and the 18th Annual Genetic Programming Conference (GP), Amsterdam, The Netherlands, July 06-10, 2013Receiver Operating Characteristics (ROC) curves represent the performance of a classifier for all possible operating con-ditions, i.e., for all preferences regarding the tradeoff be-tween false positives and false negatives. The generation of a ROC curve generally involves the training of a single classifier for a given set of operating conditions, with the subsequent use of threshold-moving to obtain a complete ROC curve. Recent work has shown that the generation of ROC curves may also be formulated as a multi-objective optimization problem in ROC space: the goals to be min-imized are the false positive and false negative rates. This technique also produces a single ROC curve, but the curve may derive from operating points for a number of different classifiers. This paper aims to provide an empirical compar-ison of the performance of both of the above approaches, for the specific case of prototype-based classifiers. Results on synthetic and real domains shows a performance advantage for the multi-objective approach.GECCO 2013 Presentation slidestext/plainapplication/pdfengROC curvesMulti-objective Machine Learningconference presentationInformáticahttps://doi.org/10.1145/2463372.2463504open access10291036GECCO'13: Proceedings of the Fifteenth International Conference on Genetic and Evolutionary Computation ConferenceCC/0000033178