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 …

Abstract Intrusion Detection Systems (IDS) have become pivotal in safeguarding information
systems against evolving threats. Concurrently, Concept Drift presents a significant …

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 …

Data streams are potentially unbounded sequences of instances arriving over time to a
classifier. Designing algorithms that are capable of dealing with massive, rapidly arriving …

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 …

This work aims to connect two rarely combined research directions, ie, non-stationary data
stream classification and data analysis with skewed class distributions. We propose a novel …

Real-world data streams pose a unique challenge to the implementation of machine
learning (ML) models and data analysis. A notable problem that has been introduced by the …

Solar energy is one of the most common and promising sources of renewable energy. In
photovoltaic (PV) systems, operators can benefit from future solar irradiance predictions for …

Continual learning from data streams is among the most important topics in contemporary
machine learning. One of the biggest challenges in this domain lies in creating algorithms …

We have witnessed in recent years an ever-growing volume of information becoming
available in a streaming manner in various application areas. As a result, there is an …