Breaking Boundaries: Active Information Acquisition Across Learning and Diagnosis

To date, the processes employed for active information acquisition during periods of learning and diagnosis have been considered as separate and have been applied in distinct phases of analysis. While active learning centers on the collection of information about training cases in order to build better predictive models, diagnosis uses fixed predictive models for guiding the collection of observations about a specific test case at hand. We introduce a model and inferential methods that bridge these phases of analysis into a holistic approach to information acquisition that considers simultaneously the extension of the predictive model and the probing of a case at hand. The bridging of active learning and real-time diagnostic feature acquisition leads to a new class of policies for learning and diagnosis.