A Survey on Active Feature Acquisition Strategies
This is an incremental survey paper that addresses the problem of efficient data collection for researchers and practitioners in machine learning.
The paper surveys active feature acquisition strategies, which aim to make accurate predictions while reducing data collection costs by selectively acquiring informative features, and reviews recent progress, challenges, and future directions in the field.
Active feature acquisition studies the challenge of making accurate predictions while limiting the cost of collecting complete data. By selectively acquiring only the most informative features for each instance, these strategies enable efficient decision-making in scenarios where data collection is expensive or time-consuming. This survey reviews recent progress in active feature acquisition, discussing common problem formulations, practical challenges, and key insights. We also highlight open issues and promising directions for future research.