  In real-world scenarios, different features have different acquisition costs
at test-time which necessitates cost-aware methods to optimize the cost and
performance trade-off. This paper introduces a novel and scalable approach for
cost-aware feature acquisition at test-time. The method incrementally asks for
features based on the available context that are known feature values. The
proposed method is based on sensitivity analysis in neural networks and density
estimation using denoising autoencoders with binary representation layers. In
the proposed architecture, a denoising autoencoder is used to handle unknown
features (i.e., features that are yet to be acquired), and the sensitivity of
predictions with respect to each unknown feature is used as a context-dependent
measure of informativeness. We evaluated the proposed method on eight different
real-world datasets as well as one synthesized dataset and compared its
performance with several other approaches in the literature. According to the
results, the suggested method is capable of efficiently acquiring features at
test-time in a cost- and context-aware fashion.
