Online Semi-Supervised Concept Drift Detection with Density Estimation

Understand

Concept drift is formally defined as the change in joint distribution of a set of input variables X and a target variable y.

• The two types of drift that are extensively studied are real drift and virtual drift where the former is the change in posterior probabilities p(y|X) while the latter is the change in distribution of X without affecting the posterior probabilities.
• Many approaches on concept drift detection either assume full availability of data labels, y or handle only the virtual drift.
• In a streaming environment, the assumption of full availability of data labels, y is questioned.