In a dynamic stream there is an assumption that the underlying process generating the
stream is non-stationary and that concepts within the stream will drift and change as the …

Abstract Concept drift refers to an alteration in the relations between input and output data in
the distribution over time. Thus, a gradual concept drift alludes to a smooth and gradual …

The critical need for classifying streaming data arises from its widespread use in real-world
industries, where analyzing continuous, dynamic, and evolving data streams accurately and …

We propose a method for learning the dynamics of the decision boundary to maintain
classification performance without additional labeled data. In various applications, such as …

The ever-growing speed at which data are generated nowadays, together with the
substantial cost of labeling processes cause Machine Learning models to face scenarios in …

Machine learning solutions usually consider that the train and test data has the same
probabilistic distribution, that is, the data is stationary. However, in streaming scenarios, data …

Adversarial machine learning has recently risen to prominence due to increased concerns
over the vulnerability of machine learning algorithms to malicious attacks. While the impact …

Continual learning approaches are useful to help a model learn new information or new
tasks sequentially, while also retaining the previously acquired information. However, such …

Recognising and reacting to change in non-stationary data-streams is a challenging task.
The majority of research in this area assumes that the true class label of incoming points are …

Existing Data Stream Mining algorithms assume the availability of labelled and balanced
data streams. However, in many real-world applications such as Robotics, Weather …