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Crowd-sourcing has become a popular means of acquiring labeled data for awide variety of tasks where humans are more accurate than computers, e.g.,labeling images, matching objects, or analyzing sentiment. However, relyingsolely on the crowd is often impractical even for data sets with thousands ofitems, due to time and cost constraints of acquiring human input (which costpennies and minutes per label). In this paper, we propose algorithms forintegrating machine learning into crowd-sourced databases, with the goal ofallowing crowd-sourcing applications to scale, i.e., to handle larger datasetsat lower costs. The key observation is that, in many of the above tasks, humansand machine learning algorithms can be complementary, as humans are often moreaccurate but slow and expensive, while algorithms are usually less accurate,but faster and cheaper.  Based on this observation, we present two new active learning algorithms tocombine humans and algorithms together in a crowd-sourced database. Ouralgorithms are based on the theory of non-parametric bootstrap, which makes ourresults applicable to a broad class of machine learning models. Our results, onthree real-life datasets collected with Amazon's Mechanical Turk, and on 15well-known UCI data sets, show that our methods on average ask humans to labelone to two orders of magnitude fewer items to achieve the same accuracy as abaseline that labels random images, and two to eight times fewer questions thanprevious active learning schemes.

Active Learning for Crowd-Sourced Databases