Team Selection For Prediction Tasks

نویسندگان

  • MohammadAmin Fazli
  • Azin Ghazimatin
  • Jafar Habibi
  • Hamid Haghshenas
چکیده

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 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.

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عنوان ژورنال:
  • J. Comb. Optim.

دوره 31  شماره 

صفحات  -

تاریخ انتشار 2016