Anomaly detection has gained considerable attention in the past couple of years. Emerging
technologies, such as the Internet of Things (IoT), are known to be among the most critical …

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 …

Data stream prediction is challenging when concepts drift, processing time, and memory
constraints come into account. Concept drift refers to changes in data distribution over time …

One popular approach employed to tackle classification problems in a static environment
consists in using a Dynamic Classifier Selection (DCS)-based method to select a custom …

Classification plays a crucial role in mining the evolving data streams. The concept drift and
concept evolution are the major issues of data streams classification, which greatly affect the …

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 …

In this study, we focus on semi-supervised data stream classification tasks. With the advent
of applications that generate vast streams of data, data stream mining algorithms are …

When adding new tasks/classes in an incremental learning scenario, the previous
recognition capabilities trained on the previous training data can be lost. In the real-life …

Novelty detection in data streams is the task of detecting concepts that were not known prior,
in streams of data. Many machine learning algorithms have been proposed to detect these …

Streaming data has become an important form in the era of big data, and the concept drift, as
one of the most important problem of it, is often studied deeply. However, similar to true …