Evaluation of Active Feature Acquisition Methods for Static Feature Settings

Machine learning (ML) methods generally assume the ready availability of the complete set of input features at deployment, typically incurring little to no cost. However, this assumption does not hold universally, especially in scenarios where feature acquisitions are associated with substantial costs. In contexts like medical diagnostics, the cost of acquiring certain features, such as X-rays, biopsies, etc. encompasses not only financial costs but also poses potential risks to patient well-being. In such cases, the cost or harm of the feature acquisition should be balanced against the predictive value of the feature. Active feature acquisition (AFA) addresses this problem by training two AI components: i) the "AFA agent," an AI system tasked with determining which features should be observed, and ii) an ML prediction model that undertakes the prediction task based on the acquired feature set. While missingness was effectively determined by, for example, a physician during the acquisition of the retrospective dataset, the missingness at the deployment of the AFA agent is determined by the AFA agent, thereby leading to a missingness distribution shift. In our companion paper [1], we formulate the problem of active feature acquisition performance evaluation (AFAPE) which addresses the task of estimating the performance an AFA agent would have at deployment, from the retrospective dataset. Consequently, upon completing the AFAPE problem, the physician will be well-informed about expected rates of incorrect diagnoses and the average costs associated with feature acquisitions when the AFA system is put into operation.