Classification in Presence of Drift and Latency

Changes in underlying distributions over time are a challenging problem in supervised learning. While this problem of drift is subject to an increasing effort in research, some definitions required for proper distinction of types of drift remain ambiguous. Furthermore, the approaches discussed in literature so far require new, labelled data for incremental model updates. However, there are domains in which such data is scarce or only available with a considerable time lag, a so-called verification latency. This issues are addressed in this paper: First, the different notations used in literature are related, and an overview over types of drift is given. Second, following the change mining paradigm, explicit models of drift are introduced. These drift models can be employed when actual, labelled data is scarce or not available at all, as they allow to anticipate changes in distributions over time. Third, an exemplary drift-adaptive learning strategy that employs such a drift model is presented: Using an expectation-maximisation algorithm, a mixture of subpopulations is tracked. As a result, the classification model can be updated using solely new, unlabelled data.