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 …

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A well-trained model will be outdated if concept drift occurs.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 describes unforeseeable changes in the underlying distribution of streaming
data overtime. Concept drift research involves the development of methodologies and …

In the past few decades, research related to concept drift learning has been increasing, and
many concept drift learning algorithms have also been developed and applied to actual data …

This paper presents recurring concept drifts (RCD), a framework that offers an alternative
approach to handle data streams that suffer from recurring concept drifts (on-line learning). It …

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

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 …

For most real‐world data streams, the concept about which data is obtained may shift from
time to time, a phenomenon known as concept drift. For most real‐world applications such …

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 …

Mining data streams for predictive analysis is one of the most interesting topics in machine
learning. With the drifting data distributions, it becomes important to build adaptive systems …