  In machine learning, crowdsourcing is an economical way to label a large
amount of data. However, the noise in the produced labels may deteriorate the
accuracy of any classification method applied to the labelled data. We propose
an enhanced nearest neighbor classifier (ENN) to overcome this issue. Two
algorithms are developed to estimate the worker quality (which is often unknown
in practice): one is to construct the estimate based on the denoised worker
labels by applying the $k$NN classifier to the expert data; the other is an
iterative algorithm that works even without access to the expert data. Other
than strong numerical evidence, our proposed methods are proven to achieve the
same regret as its oracle version based on high-quality expert data. As a
technical by-product, a lower bound on the sample size assigned to each worker
to reach the optimal convergence rate of regret is derived.
