The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …

In recent years, the availability of time series streaming information has been growing
enormously. Learning from real-time data has been receiving increasingly more attention …

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 …

Online learning involves extracting information from large quantities of data (streams)
usually affected by changes in the distribution (concept drift). A drift detector is a small …

Federated Learning (FL) under distributed concept drift is a largely unexplored area.
Although concept drift is itself a well-studied phenomenon, it poses particular challenges for …

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 …

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 …

Mining and analysing streaming data is crucial for many applications, and this area of
research has gained extensive attention over the past decade. However, there are several …

Data stream mining has become an important research area over the past decade due to the
increasing amount of data available today. Sources from various domains generate a near …

Online learning is about extracting information from large data streams which may be
affected by changes in the distribution of the data, events known as concept drift. Concept …