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 …

At present, concept drift in the nonstationary data stream is showing trends with different
speeds and different degrees of severity, which has brought great challenges to many fields …

Controlling active and passive systems in buildings with the aim of optimizing energy
efficiency and maintaining occupants' comfort is the major task of building management …

Mining high-dimensional data streams poses a fundamental challenge to machine learning
as the presence of high numbers of attributes can remarkably degrade any mining task's …

Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to
edges in an online manner, for the purpose of detecting unusual behavior, using constant …

This paper proposes a novel algorithm for incremental learning over streaming data in a non-
stationary environment. The idea refers to the applicability of Probabilistic Neural Networks …

The last few decades have witnessed a significant evolution of technology in different
domains, changing the way the world operates, which leads to an overwhelming amount of …

Learning from potentially infinite and high-dimensional data streams poses significant
challenges in the classification task. For instance, k-Nearest Neighbors (k NN) is one of the …

Data stream mining has taken over as a new field of research during past few years. It has
gained lot of attention recently due to its challenging characteristics like dynamic nature …

Automated Machine Learning (autoML) is a novel topic that aims to tackle the parameter
configuration issue using automatic monitoring models and comprises different machine …