Receiver Operating Characteristics (ROC) curves represent the performance of a classifier for all possible operating conditions, i.e., for all preferences regarding the tradeoff between 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 minimized 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 comparison 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. Copyright © 2013 ACM.
|Title of host publication
|GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference|GECCO - Proc. Genet. Evol. Comput. Conf.
|Association for Computing Machinery
|Number of pages
|Published - 2013
|2013 15th Genetic and Evolutionary Computation Conference, GECCO 2013 - Amsterdam
Duration: 1 Jul 2013 → …
|2013 15th Genetic and Evolutionary Computation Conference, GECCO 2013
|1/07/13 → …
- Multi-objective machine learning
- ROC curves