IWDA: Importance Weighting for Drift Adaptation in Streaming Supervised Learning Problems.

Distribution drift is an important issue for practical applications of machine learning (ML). In particular, in streaming ML, the data distribution may change over time, yielding the problem of concept drift, which affects the performance of learners trained with outdated data. In this article, we focus on supervised problems in an online nonstationary setting, introducing a novel learner-agnostic algorithm for drift adaptation, namely importance weighting for drift adaptation (IWDA), with the goal of performing efficient retraining of the learner when drift is detected.