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hidden contexts

In machine learning, hidden contexts refer to unobserved or unmeasured environmental conditions that influence the relationship between input data and target outcomes. Because these underlying factors are not explicitly recorded as features in a dataset, changes in hidden contexts often cause concept drift, altering the statistical properties of the target concept over time. Learning systems operating in these dynamic environments must detect when unmodeled situational shifts occur, adapt to changes by discarding outdated training patterns, and potentially re-use previously learned models when a recurring hidden context reappears.

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Learning in the Presence of Concept Drift and Hidden Contexts

Learning in the Presence of Concept Drift and Hidden Contexts

G. Widmer, M. Kubát

OrganizationsAustrian Research Institute for Artificial IntelligenceUniversity of OttawaUniversity of Vienna

Why you should read this

Presents the FLORA framework of incremental learning algorithms that dynamically adjust sample windows and reuse past concept descriptions to handle recurring hidden contexts and concept drift in continuous data streams.

On-line learning in domains where the target concept depends on some hidden context poses serious problems. A changing context can induce changes in the target concepts, producing what is known as concept drift. We describe a family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear. The general approach underlying all these algorithms consists of (1) keeping only a window of currently trusted examples and hypotheses; (2) storing concept descriptions and re-using them when a previous context re-appears; and (3) controlling both of these functions by a heuristic that constantly monitors the system's behavior. The paper reports on experiments that test the systems' performance under various conditions such as different levels of noise and different extent and rate of concept drift.

Added

2026-09-24