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Composing ensembles by a stochastic approach under execution time constraint

Ensemble-based systems are primarily analyzed on how the accuracy of the ensemble depends on that of its members. In this paper, we extend this model with adding a natural constraint regarding a time limit within which the ensemble should make the decision. For this aim, we consider both the execution time and accuracy of each member. Then, we solve the problem on how to find the most accurate ensemble, where the sum of the execution times of its members remains below the limit. As a decision rule, we analyze a majority voting-based one generalized to be applicable in single object detection scenarios. The optimization task leads to a non-separable Knapsack problem, which is addressed using stochastic considerations. The proposed methodology is also validated experimentally for the localization of the optic disc in retinal images.