In recent years, the availability of time series streaming information has been growing
enormously. Learning from real-time data has been receiving increasingly more attention …

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

Mining and analysing streaming data is crucial for many applications, and this area of
research has gained extensive attention over the past decade. However, there are several …

Many real‐world data mining applications have to deal with unlabeled streaming data. They
are unlabeled because the sheer volume of the stream makes it impractical to label a …

Abstract Concept drift and shift are major issues that greatly affect the accuracy and
reliability of many real-world applications of machine learning. We propose a new data …

Data-driven decision-making (D^ 3 D 3 M) is often confronted by the problem of uncertainty
or unknown dynamics in streaming data. To provide real-time accurate decision solutions …

One of the most challenging problems in the field of online learning is concept drift, which
deeply influences the classification stability of streaming data. If the data stream is …

In stream learning, data continuously arrives over time, often at a very high rate. For
imbalanced data streams with concept drift, it becomes essential to simultaneously address …

Concept drift refers to changes in the distribution of underlying data and is an inherent
property of evolving data streams. Ensemble learning, with dynamic classifiers, has proved …

Drift detection methods identify changes in data streams. Such changes are called concept
drifts. Existing drift detection methods often assume that the input is a noise-free data stream …