Team Selection For Prediction Tasks

TL;DR

This paper introduces a method for selecting an optimal subset of experts to predict outcomes, modeling the problem as an NP-hard integer quadratic program and proposing heuristics like tabu search for practical solutions.

Contribution

It formulates the team selection problem for prediction tasks as an integer quadratic programming problem and demonstrates effective heuristics for solving it.

Findings

The team selection problem is NP-hard.

Relaxation of the problem is solvable in polynomial time.

Tabu search heuristic performs effectively in experiments.

Abstract

Given a random variable $O \in \mathbb{R}$ and a set of experts $E$, we describe a method for finding a subset of experts $S \subseteq E$ whose aggregated opinion best predicts the outcome of $O$. 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 $k$ turns) can be modeled with an integer quadratic programming and we prove its NP-hardness whereas its relaxation is solvable in polynomial time. Finally, we do an experimental comparison between different rounding and greedy heuristics and show that our suggested tabu search works effectively. Keywords: Team Selection, Information Aggregation, Opinion Pooling, Quadratic Programming, NP-Hard