Retrieving, analyzing, and processing large data can be challenging. An effective and
efficient mechanism for overcoming these challenges is to cluster the data into a compact …

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 …

Novelty detection in data streams is the task of detecting concepts that were not known prior,
in streams of data. Many machine learning algorithms have been proposed to detect these …

Data production has followed an increased growth in the last years, to the point that
traditional or batch machine-learning (ML) algorithms cannot cope with the sheer volume of …

Stream Learning (SL) attempts to learn from a data stream efficiently. A data stream learning
algorithm should adapt to input data distribution shifts without sacrificing accuracy. These …

Data stream prediction is challenging when concepts drift, processing time, and memory
constraints come into account. Concept drift refers to changes in data distribution over time …

Abstract Intrusion Detection Systems (IDS) help protect computer networks by identifying
and detecting attempts to obtain unauthorized access to data via computer networks by …

In dynamic environments, making classification decisions based on classical intelligent
decision support systems is a challenge, as the classification performance of decision …

Learning from unlabeled data or self-learning, can substantially reduce the complexity of
machine learning (ML) utilization in real-time deployment. While the development of …

Distributed online learning has been proven extremely effective in solving large-scale
machine learning problems over streaming data. However, information sharing between …