Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification
This work addresses the challenge of cost-effective feature selection in machine learning, particularly for applications like healthcare, though it is incremental as it builds on existing RL and set encoding methods.
The paper tackles the problem of active feature acquisition by formulating it as a reinforcement learning task and introducing a joint learning framework for the RL agent and classifier, achieving improved prediction performance and reduced feature acquisition costs on synthetic and real datasets like EHR.
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.