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

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)... (read more)

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