  As part of a quality control process in manufacturing it is often necessary
to test whether all parts of a product satisfy a required property, with as few
inspections as possible. When multiple inspection apparatuses with different
costs and precision exist, it is desirable that testing can be carried out
cost-effectively by properly controlling the trade-off between the costs and
the precision. In this paper, we formulate this as a level set estimation (LSE)
problem under cost-dependent input uncertainty - LSE being a type of active
learning for estimating the level set, i.e., the subset of the input space in
which an unknown function value is greater or smaller than a pre-determined
threshold. Then, we propose a new algorithm for LSE under cost-dependent input
uncertainty with theoretical convergence guarantee. We demonstrate the
effectiveness of the proposed algorithm by applying it to synthetic and real
datasets.
