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Journal of Combinatorial Optimization

Erschienen in:

01.02.2016

Team selection for prediction tasks

verfasst von: MohammadAmin Fazli, Azin Ghazimatin, Jafar Habibi, Hamid Haghshenas

Erschienen in: Journal of Combinatorial Optimization | Ausgabe 2/2016

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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 \(k\) 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.

Vorheriger Artikel Power domination with bounded time constraints

Nächster Artikel Degree-constrained orientations of embedded graphs

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Titel: Team selection for prediction tasks
verfasst von: MohammadAmin Fazli
Azin Ghazimatin
Jafar Habibi
Hamid Haghshenas
Publikationsdatum: 01.02.2016
Verlag: Springer US
Erschienen in: Journal of Combinatorial Optimization / Ausgabe 2/2016
Print ISSN: 1382-6905
Elektronische ISSN: 1573-2886
DOI: https://doi.org/10.1007/s10878-014-9784-3

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