Definition
Concept drift occurs when the values of hidden variables change over time. That is, there is some unknown context for concept learning and when that context changes, the learned concept may no longer be valid and must be updated or relearned.
Motivation and Background
Prediction in real-world domains is complicated by potentially unstable phenomena that are not known in advance to the learning system. For example, financial market behavior can change dramatically with changes in contract prices, interest rates, inflation rates, budget announcements, and political and world events. Thus, concept definitions that may have been learned in one context become invalid in a new context. This concept drift can be due to changes in context and is often directly reflected by one or more attributes. When changes in context are not reflected by any known attributes they can be said to be hidden. Hidden changes in context...
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Sammut, C., Harries, M. (2017). Concept Drift. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_153
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