  Label noise in data has long been an important problem in supervised learning
applications as it affects the effectiveness of many widely used classification
methods. Recently, important real-world applications, such as medical diagnosis
and cybersecurity, have generated renewed interest in the Neyman-Pearson (NP)
classification paradigm, which constrains the more severe type of error (e.g.,
the type I error) under a preferred level while minimizing the other (e.g., the
type II error). However, there has been little research on the NP paradigm
under label noise. It is somewhat surprising that even when common NP
classifiers ignore the label noise in the training stage, they are still able
to control the type I error with high probability. However, the price they pay
is excessive conservativeness of the type I error and hence a significant drop
in power (i.e., $1 - $ type II error). Assuming that domain experts provide
lower bounds on the corruption severity, we propose the first theory-backed
algorithm that adapts most state-of-the-art classification methods to the
training label noise under the NP paradigm. The resulting classifiers not only
control the type I error with high probability under the desired level but also
improve power.
