Incremental learning, online learning, and data stream learning are terms commonly
associated with learning algorithms that update their models given a continuous influx of …

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

Concept drift poses a critical challenge to deploy machine learning models to solve practical
security problems. Due to the dynamic behavior changes of attackers (and/or the benign …

The amount of real-time communication between agents in an information system has
increased rapidly since the beginning of the decade. This is because the use of these …

The world surrounding us is subject to constant change. These changes, frequently
described as concept drift, influence many industrial and technical processes. As they can …

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 …

Many real-world applications adopt multi-label data streams as the need for algorithms to
deal with rapidly changing data increases. Changes in data distribution, also known as …

The data stream poses additional challenges to statistical classification tasks because
distributions of the training and target samples may differ as time passes. Such a distribution …

In the widespread application of industrial defect detection supported by neural networks,
changes in the characteristics of industrial site data affect the model performance. To …

Unlabelled data appear in many domains and are particularly relevant to streaming
applications, where even though data is abundant, labelled data is rare. To address the …