In many real-world applications, data are often collected in the form of a stream, and thus the
distribution usually changes in nature, which is referred to as concept drift in the literature …

In many real-world scenarios, we often deal with streaming data that is sequentially
collected over time. Due to the non-stationary nature of the environment, the streaming data …

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A well-trained model will be outdated if concept drift occurs.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 …

Abstract In the “Big Data” age, the amount and distribution of data have increased wildly and
changed over time in various time-series-based tasks, eg weather prediction, network …

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 most challenging data analysis applications, data evolve over time and must be analyzed
in near real time. Patterns and relations in such data often evolve over time, thus, models …

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

Many estimation, prediction, and learning applications have a dynamic nature. One of the
most important challenges in machine learning is dealing with concept changes. Underlying …

Concept drift techniques aim at learning patterns from data streams that may change over
time. Although such behavior is not usually expected in controlled environments, real-world …

With the advent of the fourth industrial revolution, data-driven decision making has also
become an integral part of decision making. At the same time, deep learning is one of the …