  In many real-world machine learning problems, feature values are not readily
available. To make predictions, some of the missing features have to be
acquired, which can incur a cost in money, computational time, or human time,
depending on the problem domain. This leads us to the problem of choosing which
features to use at the prediction time. The chosen features should increase the
prediction accuracy for a low cost, but determining which features will do that
is challenging. The choice should take into account the previously acquired
feature values as well as the feature costs. This paper proposes a novel
approach to address this problem. The proposed approach chooses the most useful
features adaptively based on how relevant they are for the prediction task as
well as what the corresponding feature costs are. Our approach uses a generic
neural network architecture, which is suitable for a wide range of problems. We
evaluate our approach on three cost-sensitive datasets, including Yahoo!
Learning to Rank Competition dataset as well as two health datasets. We show
that our approach achieves high accuracy with a lower cost than the current
state-of-the-art approaches.
