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Team selection for prediction tasks

Fazli, M. A ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1007/s10878-014-9784-3
  3. Publisher: Springer New York LLC
  4. Abstract:
  5. Given a random variable O ∈ R and a set of experts E, we describe a method for finding a subset of experts S ⊆ 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 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
  6. Keywords:
  7. Polynomial approximation ; Quadratic programming ; Tabu search ; Artificial datasets ; Experimental comparison ; Greedy heuristics ; Information aggregation ; Informative ness ; NP-hard ; Opinion pooling ; Team Selection ; Integer programming
  8. Source: Journal of Combinatorial Optimization ; Volume 31, Issue 2 , 2016 , Pages 743-757 ; 13826905 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s10878-014-9784-3