Active feature acquisition (AFA) studies how a predictive system can sequentially choose which feature values to obtain for each instance to balance predictive accuracy against feature acquisition cost (financial, time, invasiveness, or privacy). This survey provides the first unified treatment of modern AFA through an explicit MDP and POMDP formulation, showing that most existing methods can be understood as different approximations of the same underlying sequential decision problem. The survey proposes an up-to-date taxonomy organizing AFA into three families: (i) embedded cost-aware predictors (notably cost-sensitive decision trees and ensembles), (ii) model-based methods that plan using learned probabilistic components, and (iii) model-free or hybrid methods that learn policies from simulated acquisition episodes. We hope this POMDP-centric view both clarifies existing work and motivates new AFA methods that more directly build on the mature literature on POMDP planning and approximation. It concludes by outlining open challenges for achieving robust cost–accuracy trade-offs in practice, including reliable evaluation under realistic missingness and logging, computational constraints, and deployment requirements such as robustness and interpretability.
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