Team selection for prediction tasks

Given a random variable $$O \in \mathbb {R}$$ O ∈ R and a set of experts $$E$$ E , we describe a method for finding a subset of experts $$S \subseteq E$$ S ⊆ E whose aggregated opinion best predicts the outcome of $$O$$ O . Therefore, the problem can be regarded as a team formation for performing a prediction task. We show that in case of aggregating experts’ opinions by simple averaging, finding the best team (the team with the lowest total error during past $$k$$ k rounds) can be modeled with an integer quadratic programming and we prove its NP-hardness whereas its relaxation is solvable in polynomial time. At the end, we do an experimental comparison between different rounding and greedy heuristics on artificial datasets which are generated based on calibration and informativeness of exprets’ information and show that our suggested tabu search works effectively.