  In many real-world learning scenarios, features are only acquirable at a cost
constrained under a budget. In this paper, we propose a novel approach for
cost-sensitive feature acquisition at the prediction-time. The suggested method
acquires features incrementally based on a context-aware feature-value
function. We formulate the problem in the reinforcement learning paradigm, and
introduce a reward function based on the utility of each feature. Specifically,
MC dropout sampling is used to measure expected variations of the model
uncertainty which is used as a feature-value function. Furthermore, we suggest
sharing representations between the class predictor and value function
estimator networks. The suggested approach is completely online and is readily
applicable to stream learning setups. The solution is evaluated on three
different datasets including the well-known MNIST dataset as a benchmark as
well as two cost-sensitive datasets: Yahoo Learning to Rank and a dataset in
the medical domain for diabetes classification. According to the results, the
proposed method is able to efficiently acquire features and make accurate
predictions.
