Concept drift refers to changes in the underlying data distribution of data streams over time.
A well-trained model will be outdated if concept drift occurs. Once concept drift is detected, it …

Concept drift is a phenomenon where the distribution of data streams changes over time.
When this happens, model predictions become less accurate. Hence, models built in the …

In a data stream, concept drift refers to unpredictable distribution changes over time, which
violates the identical-distribution assumption required by conventional machine learning …

Abstract Concept drift poses significant challenges in the fields of machine learning and data
mining. At present, many existing algorithms struggle to maintain low error rates or require …

At present, concept drift in the nonstationary data stream is showing trends with different
speeds and different degrees of severity, which has brought great challenges to many fields …

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 …

The change in data distribution over time (known as concept drift) makes the classification
process complex because of the discrepancy between current and incoming data …

Concept drift describes unforeseeable changes in the underlying distribution of streaming
data overtime. Concept drift research involves the development of methodologies and …

Adapting to drifting data streams is a significant challenge in online learning. Concept drift
must be detected for effective model adaptation to evolving data properties. Concept drift …

In concept drift adaptation, we aim to design a blind or an informed strategy to update our
best predictor for future data at each time point. However, existing informed drift adaptation …