Abstract Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to
derive insights from clinical data and improve patient outcomes. However, these highly …

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

River is a machine learning library for dynamic data streams and continual learning. It
provides multiple state-of-the-art learning methods, data generators/transformers …

Class imbalance poses new challenges when it comes to classifying data streams. Many
algorithms recently proposed in the literature tackle this problem using a variety of data …

Applications that generate huge amounts of data in the form of fast streams are becoming
increasingly prevalent, being therefore necessary to learn in an online manner. These …

In recent years, with the increasing popularity of “Smart Technology”, the number of Internet
of Things (IoT) devices and systems have surged significantly. Various IoT services and …

The widespread usage of machine learning in different mainstream contexts has made deep
learning the technique of choice in various domains, including finance. This systematic …

The significant growth of interconnected Internet‐of‐Things (IoT) devices, the use of social
networks, along with the evolution of technology in different domains, lead to a rise in the …

Modern networks generate a massive amount of traffic data streams. Analyzing this data is
essential for various purposes, such as network resources management and cyber-security …

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 …