Methods for Designing Multiple Classifier Systems

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Practical Evasion of a Learning-Based Classifier: A Case Study (2014, methodology): “Finally, methods based on multiple classifier systems [45] should be mentioned as a potential solution.”
Getting the Most Out of Ensemble Selection (2006, methodology): “Ensemble selection employs greedy forward selection to select models to add to the ensemble, a method categorized in the literature as overproduce and choose [11].”
Ensembles of Learning Machines (2002, methodology): “Different selection methods based on different search algorithm mututated from feature selection methods (forward and backward search) or for the solution of complex optimization tasks (tabu search) are proposed in [109].”
Machine learning: a review of classification and combining techniques (2006, methodology): “Voting denotes the simplest method of combining predictions from multiple classifiers (Roli et al. 2001).”