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 …

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 …

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 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 …

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 …

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 …

Abstract Machine learning based intrusion detection systems monitor network data streams
for cyber attacks. Challenges in this space include detecting unknown attacks, adapting to …

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

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

Adaptive machine learning (ML) aims to allow ML models to adapt to ever-changing
environments with potential concept drift after model deployment. Traditionally, adaptive ML …