Online learning regards extracting information from large quantities of data (streams) usually
affected by changes in the distribution (concept drift). Drift detectors are software that …

Online learning involves extracting information from large quantities of data (streams)
usually affected by changes in the distribution (concept drift). A drift detector is a small …

Abstract Machine learning models assume that data is drawn from a stationary distribution.
However, in practice, challenges are imposed on models that need to make sense of fast …

Abstract Concept drift detectors are software that usually attempt to estimate the positions of
concept drifts in large data streams in order to replace the base learner after changes in the …

Abstract Concept drift detectors are online learning software that mostly attempt to estimate
the drift positions in data streams in order to modify the base classifier after these changes …

The majority of online learners assume that the data distribution to be learned is established
in advance. There are many real-world problems where the distribution of the data changes …

When using statistical models (such as a classifier) in a streaming environment, there is
often a need to detect and adapt to concept drifts to mitigate any deterioration in the model's …

In data stream environments, drift detection methods are used to identify when the context
has changed. This paper evaluates eight different concept drift detectors (ddm, eddm, pht …

An established method to detect concept drift in data streams is to perform statistical
hypothesis testing on the multivariate data in the stream. The statistical theory offers rank …

Practical applications involving big data, such as weather monitoring, identification of
customer preferences, Internet log analysis, and sensors warnings require challenging data …