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To achieve a reliable detection, we propose a combination of multiple classifiers, and we compare three alternative strategies to fuse the results of each classifier, namely: (i) induced order weighted averaging operators, (ii) genetic algorithms, and (iii) particle swarm optimization. Each method is aimed at determining the optimal weights to be assigned to the decision scores yielded by different deep models, according to the relevant optimization strategy. Experimental tests have been performed on three event recognition datasets, evaluating the performance of various deep models, both alone and selectively combined. 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