A selection approach for scalable fuzzy integral combination

We consider the problem of collective decision-making from an arbitrary set of classifiers under Sugeno fuzzy integral (S-FI). 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 S-FI, and the typical S-FI combination of selected classifiers. The proposed selection is based on a greedy algorithm which through a heuristic allows an efficient search.