{"ID":2880281,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.14734","arxiv_id":"2508.14734","title":"AFABench: A Generic Framework for Benchmarking Active Feature Acquisition","abstract":"In many real-world scenarios, acquiring all features of a data instance can be expensive or impractical due to monetary cost, latency, or privacy concerns. Active Feature Acquisition (AFA) addresses this challenge by dynamically selecting a subset of informative features for each data instance, trading predictive performance against acquisition cost. While numerous methods have been proposed for AFA, ranging from myopic information-theoretic strategies to non-myopic reinforcement learning approaches, fair and systematic evaluation of these methods has been hindered by a lack of standardized benchmarks. In this paper, we introduce AFABench, the first benchmark framework for AFA. Our benchmark includes a diverse set of synthetic and real-world datasets, supports a wide range of acquisition policies, and provides a modular design that enables easy integration of new methods and tasks. We implement and evaluate representative algorithms from all major categories, including static, myopic, and reinforcement learning-based approaches. To test the lookahead capabilities of AFA policies, we introduce a novel synthetic dataset, CUBE-NM, designed to expose the limitations of myopic selection. Our results highlight key trade-offs between different AFA strategies and provide actionable insights for future research. The benchmark code is available at: https://github.com/Linusaronsson/AFA-Benchmark.","short_abstract":"In many real-world scenarios, acquiring all features of a data instance can be expensive or impractical due to monetary cost, latency, or privacy concerns. Active Feature Acquisition (AFA) addresses this challenge by dynamically selecting a subset of informative features for each data instance, trading predictive perfo...","url_abs":"https://arxiv.org/abs/2508.14734","url_pdf":"https://arxiv.org/pdf/2508.14734v3","authors":"[\"Valter Schütz\",\"Han Wu\",\"Reza Rezvan\",\"Linus Aronsson\",\"Morteza Haghir Chehreghani\"]","published":"2025-08-20T14:29:16Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[\"Reinforcement Learning\"]","has_code":false,"code_links":[{"ID":610664,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2880281,"paper_url":"https://arxiv.org/abs/2508.14734","paper_title":"AFABench: A Generic Framework for Benchmarking Active Feature Acquisition","repo_url":"https://github.com/Linusaronsson/AFA-Benchmark","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}