DEAL: Data-Efficient Active Learning for Regression Under Drift

Current work on Active Learning (AL) tends to assume that the relationship between input and target variables does not change, i.e., the oracle is static. However, oracles can be stream-like and exhibit concept drift, which requires updating the learned relationship. Standard drift detection and adaption methods rely on constantly observing the target variables, which is too costly in AL. Current work on AL for regression has not addressed the challenge of frequently drifting oracles. We propose a new AL method that estimates its error due to drift by learning statistics about how often and how severe drift occurs, based on a Gaussian Process model with a time-variant kernel. Whenever the estimated error reaches a user-required threshold, our model measures the target variables and recalibrates the learned relationship as well as the drift statistics. Our drift-aware model requires up to 20 times fewer measurements than widely used methods.