  Crowdsourcing provides a popular paradigm for data collection at scale. We
study the problem of selecting subsets of workers from a given worker pool to
maximize the accuracy under a budget constraint. One natural question is
whether we should hire as many workers as the budget allows, or restrict on a
small number of top-quality workers. By theoretically analyzing the error rate
of a typical setting in crowdsourcing, we frame the worker selection problem
into a combinatorial optimization problem and propose an algorithm to solve it
efficiently. Empirical results on both simulated and real-world datasets show
that our algorithm is able to select a small number of high-quality workers,
and performs as good as, sometimes even better than, the much larger crowds as
the budget allows.
