Team selection for prediction tasks

MohammadAmin Fazli (), Azin Ghazimatin (), Jafar Habibi () and Hamid Haghshenas ()
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MohammadAmin Fazli: Sharif University of Technology
Azin Ghazimatin: Sharif University of Technology
Jafar Habibi: Sharif University of Technology
Hamid Haghshenas: Sharif University of Technology

Journal of Combinatorial Optimization, 2016, vol. 31, issue 2, No 17, 743-757

Abstract: Abstract 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.

Keywords: Team Selection; Information aggregation; Opinion pooling; Quadratic programming; NP-hard (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10878-014-9784-3

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