Title: Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification,
Abstract: We consider the problem of active feature acquisition, where we sequentially
select the subset of features in order to achieve the maximum prediction
performance in the most cost-effective way. In this work, we formulate this
active feature acquisition problem as a reinforcement learning problem, and
provide a novel framework for jointly learning both the RL agent and the
classifier (environment). We also introduce a more systematic way of encoding
subsets of features that can properly handle innate challenge with missing
entries in active feature acquisition problems, that uses the orderless
LSTM-based set encoding mechanism that readily fits in the joint learning
framework. We evaluate our model on a carefully designed synthetic dataset for
the active feature acquisition as well as several real datasets such as
electric health record (EHR) datasets, on which it outperforms all baselines in
terms of prediction performance as well feature acquisition cost.