Sequential Cost-Sensitive Feature Acquisition
This work addresses the challenge of optimizing feature acquisition costs in machine learning, which is incremental as it combines existing representation and reinforcement learning techniques.
The paper tackles the problem of cost-sensitive feature acquisition by proposing a reinforcement learning approach that adaptively selects features based on their costs, achieving effective results in both sparse prediction and cost-sensitive settings across various datasets.
We propose a reinforcement learning based approach to tackle the cost-sensitive learning problem where each input feature has a specific cost. The acquisition process is handled through a stochastic policy which allows features to be acquired in an adaptive way. The general architecture of our approach relies on representation learning to enable performing prediction on any partially observed sample, whatever the set of its observed features are. The resulting model is an original mix of representation learning and of reinforcement learning ideas. It is learned with policy gradient techniques to minimize a budgeted inference cost. We demonstrate the effectiveness of our proposed method with several experiments on a variety of datasets for the sparse prediction problem where all features have the same cost, but also for some cost-sensitive settings.