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Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling
[article]
2014
arXiv
pre-print
In crowd labeling, a large amount of unlabeled data instances are outsourced to a crowd of workers. Workers will be paid for each label they provide, but the labeling requester usually has only a limited amount of the budget. Since data instances have different levels of labeling difficulty and workers have different reliability, it is desirable to have an optimal policy to allocate the budget among all instance-worker pairs such that the overall labeling accuracy is maximized. We consider
arXiv:1403.3080v2
fatcat:tntusblngnegpoqiatzyk4nrwq