Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams
This addresses the challenge of constrained feature costs for real-world streaming applications, representing an incremental improvement in cost-sensitive learning methods.
The paper tackles the problem of cost-sensitive feature acquisition under a budget in data streams by proposing an online reinforcement learning approach that uses MC dropout to measure feature utility, achieving efficient feature acquisition and accurate predictions on datasets like MNIST, Yahoo Learning to Rank, and a medical diabetes dataset.
In many real-world learning scenarios, features are only acquirable at a cost constrained under a budget. In this paper, we propose a novel approach for cost-sensitive feature acquisition at the prediction-time. The suggested method acquires features incrementally based on a context-aware feature-value function. We formulate the problem in the reinforcement learning paradigm, and introduce a reward function based on the utility of each feature. Specifically, MC dropout sampling is used to measure expected variations of the model uncertainty which is used as a feature-value function. Furthermore, we suggest sharing representations between the class predictor and value function estimator networks. The suggested approach is completely online and is readily applicable to stream learning setups. The solution is evaluated on three different datasets including the well-known MNIST dataset as a benchmark as well as two cost-sensitive datasets: Yahoo Learning to Rank and a dataset in the medical domain for diabetes classification. According to the results, the proposed method is able to efficiently acquire features and make accurate predictions.