Data streams may encounter data distribution changes, which can significantly impair the
accuracy of models. Concept drift detection tracks data distribution changes and signals …

In data stream mining, the emergence of new patterns or a pattern ceasing to exist is called
concept drift. Concept drift makes the learning process complicated because of the …

Abstract Concept drifts usually originate from many causes instead of only one, which result
in two types of concept drifts: abrupt drifts and gradual drifts. From the point of view of speed …

In data stream mining, one of the biggest challenges is to develop algorithms that deal with
the changing data. As data evolve over time, static models become outdated. This …

Data stream mining has become an important research area over the past decade due to the
increasing amount of data available today. Sources from various domains generate a near …

Abstract Concept drift refers to the inevitable phenomenon that influences the statistical
features of the data stream. Detecting concept drift in data streams quickly and precisely …

Abstract Concept Drift is considered a challenging problem that appears in data streaming.
The classifier's error rate and the ensemble are used in most of the previous works to …

Many methods have been proposed to detect concept drift, ie, the change in the distribution
of streaming data, due to concept drift causes a decrease in the prediction accuracy of …

Detecting changes in data-streams is an important part of enhancing learning quality in
dynamic environments. We devise a procedure for detecting concept drifts in data-streams …

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 …