Human-in-the-loop Handling of Knowledge Drift
This addresses drift handling in hierarchical classification for applications like smart personal assistants, but it is incremental as it builds on existing drift detection methods by adding user interaction.
The paper tackles the problem of knowledge drift in hierarchical classification, where concept vocabularies, distributions, and relations change over time, by introducing TRCKD, a human-in-the-loop approach that combines automated detection with user queries to disambiguate drift types, showing that a handful of queries substantially improves prediction performance on synthetic and realistic data.
We introduce and study knowledge drift (KD), a complex form of drift that occurs in hierarchical classification. Under KD the vocabulary of concepts, their individual distributions, and the is-a relations between them can all change over time. The main challenge is that, since the ground-truth concept hierarchy is unobserved, it is hard to tell apart different forms of KD. For instance, introducing a new is-a relation between two concepts might be confused with individual changes to those concepts, but it is far from equivalent. Failure to identify the right kind of KD compromises the concept hierarchy used by the classifier, leading to systematic prediction errors. Our key observation is that in many human-in-the-loop applications (like smart personal assistants) the user knows whether and what kind of drift occurred recently. Motivated by this, we introduce TRCKD, a novel approach that combines automated drift detection and adaptation with an interactive stage in which the user is asked to disambiguate between different kinds of KD. In addition, TRCKD implements a simple but effective knowledge-aware adaptation strategy. Our simulations show that often a handful of queries to the user are enough to substantially improve prediction performance on both synthetic and realistic data.