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

Although the existing machine learning-based intrusion detection systems in the Internet of
Things (IoT) usually perform well in static environments, they struggle to preserve their …

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 …

Abstract Concept drift is a phenomenon that commonly happened in data streams and need
to be detected, because it means the statistical properties of a target variable, which the …

Federated Learning is a novel framework that allows multiple devices or institutions to train a
machine learning model collaboratively while preserving their data private. This …

In a non-stationary environment, newly received data may have different knowledge patterns
from the data used to train learning models. As time passes, a learning model's performance …

In our society, many fields have produced a large number of data streams. How to mining
the interesting knowledge and patterns from continuous data stream becomes a problem …

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 …

Data streams may encounter data distribution changes, which can significantly impair the
accuracy of models. Concept drift detection tracks data distribution changes and signals …

Data streams classification is an important approach to get useful knowledge from massive
and dynamic data. Because of concept drift, traditional data mining techniques cannot be …