Classification with Costly Features using Deep Reinforcement Learning
This work addresses the challenge of optimizing feature acquisition costs in classification tasks, which is important for applications like medical diagnosis or finance, but it is incremental as it builds on prior reinforcement learning methods by replacing linear approximations with neural networks.
The paper tackles the problem of classification with costly features by framing it as a sequential decision-making task and using deep reinforcement learning with neural networks instead of linear approximations. The result is a flexible approach that achieves performance comparable to state-of-the-art methods on eight datasets, with robustness across all evaluated datasets.
We study a classification problem where each feature can be acquired for a cost and the goal is to optimize a trade-off between the expected classification error and the feature cost. We revisit a former approach that has framed the problem as a sequential decision-making problem and solved it by Q-learning with a linear approximation, where individual actions are either requests for feature values or terminate the episode by providing a classification decision. On a set of eight problems, we demonstrate that by replacing the linear approximation with neural networks the approach becomes comparable to the state-of-the-art algorithms developed specifically for this problem. The approach is flexible, as it can be improved with any new reinforcement learning enhancement, it allows inclusion of pre-trained high-performance classifier, and unlike prior art, its performance is robust across all evaluated datasets.