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

The problem of concept drift has gained a lot of attention in recent years. This aspect is key
in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks …

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 …

An intrusion detection system (IDS) is an active research topic and is regarded as one of the
important applications of machine learning. An IDS is a classifier that predicts the class of …

Developing effective and efficient data stream classifiers is challenging for the machine
learning community because of the dynamic nature of data streams. As a result, many data …

Learning from streaming data is challenging as the distribution of incoming data may
change over time, a phenomenon known as concept drift. The predictive patterns, or …

Data stream mining is an important research topic that has received increasing attention due
to its use in a wide range of applications, such as sensor networks, banking, and …

Almost all drift detection mechanisms designed for classification problems work reactively:
after receiving the complete data set (input patterns and class labels) they apply a sequence …

Machine Learning (ML) has been widely applied to cybersecurity and is considered state-of-
the-art for solving many of the open issues in that field. However, it is very difficult to evaluate …

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 …