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We consider the problem of active feature acquisition, where we sequentiallyselect the subset of features in order to achieve the maximum predictionperformance in the most cost-effective way. In this work, we formulate thisactive feature acquisition problem as a reinforcement learning problem, andprovide a novel framework for jointly learning both the RL agent and theclassifier (environment). We also introduce a more systematic way of encodingsubsets of features that can properly handle innate challenge with missingentries in active feature acquisition problems, that uses the orderlessLSTM-based set encoding mechanism that readily fits in the joint learningframework. We evaluate our model on a carefully designed synthetic dataset forthe active feature acquisition as well as several real datasets such aselectric health record (EHR) datasets, on which it outperforms all baselines interms of prediction performance as well feature acquisition cost.

Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification