Multi-objective evolutionary optimization for generating ensembles of classifiers in the ROC space
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Local expert forest of score fusion for video event classification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Journal of Signal Processing Systems
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An ensemble learning framework is proposed to optimize the receiver operating characteristic (ROC) curve corresponding to a given classifier. The proposed ensemble maximal figure-ofmerit (E-MFoM) learning framework meets four key requirements desirable for ROC optimization, namely: (1) each classifier in the ensemble can be learned with any specified performance metric for any given classifier design; (2) such a classifier is discriminative in nature and attempts to optimize a particular operating point on the ROC curve of the classifier; (3) an ensemble approximation to the overall behavior of the ROC curve can be established by sampling a set of operating points; and (4) ensemble decision rules can be formulated by grouping these sampled classifiers with a uniform scoring function. We evaluate the proposed framework using 3 testing databases, the Reuters and two UCI sets. Our experimental results clearly show that E-MFoM learning outperforms the state-of-the-art algorithms using Wilcoxon-Mann-Whitney rank statistics.