A selection approach for scalable fuzzy integral combination

We consider the problem of collective decision-making from an arbitrary set of classifiers under the Sugeno fuzzy integral (SFI). We assume that classifiers are given, i.e., they cannot be modified towards their effective combination. Under this baseline, we propose a selection-combination strategy, which separates the whole process into two stages: the classifiers selection, to discover a subset of cooperative classifiers under SFI, and the typical SFI combination of selected classifiers. The proposed selection is based on a greedy algorithm which through a heuristic allows an efficient search.