The Lagrangian Structure of Cost-Penalized Active Feature Acquisition
Read the original on arXiv Machine Learning →The paper investigates Active Feature Acquisition (AFA), where features are selected sequentially to maximize predictive accuracy within a budget. It introduces a theoretical certificate that decomposes the gap between a greedy Expected Information Gain minus cost (EIG-Cost) policy and the optimal hard‑budget policy into a budget‑slack term and a greedy‑suboptimality term, δ_λ. Experiments on synthetic data, standard benchmarks, and a large clinical dataset (MIMIC‑IV) show that δ_λ is close to zero in many cases, and that EIG‑Cost achieves competitive performance while using only a small fraction of the full feature cost.
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