Title: Concept Drift Learning with Alternating Learners,
Abstract: Data-driven predictive analytics are in use today across a number of
industrial applications, but further integration is hindered by the requirement
of similarity among model training and test data distributions. This paper
addresses the need of learning from possibly nonstationary data streams, or
under concept drift, a commonly seen phenomenon in practical applications. A
simple dual-learner ensemble strategy, alternating learners framework, is
proposed. A long-memory model learns stable concepts from a long relevant time
window, while a short-memory model learns transient concepts from a small
recent window. The difference in prediction performance of these two models is
monitored and induces an alternating policy to select, update and reset the two
models. The method features an online updating mechanism to maintain the
ensemble accuracy, and a concept-dependent trigger to focus on relevant data.
Through empirical studies the method demonstrates effective tracking and
prediction when the steaming data carry abrupt and/or gradual changes.