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 …

Classification methods for streaming data are not new, but very few current frameworks
address all three of the most common problems with these tasks: concept drift, noise, and …

One of the most challenging problems in the field of online learning is concept drift, which
deeply influences the classification stability of streaming data. If the data stream is …

This article studies a new problem of data stream clustering, namely, multiview data stream
(MVStream) clustering. Although many data stream clustering algorithms have been …

This paper explores a new online learning problem where the input sequence lives in an
over-time varying feature space and the ground-truth label of any input point is given only …

Purpose One of the significant problems in data stream classification is the concept drift
phenomenon, which consists of the change in probabilistic characteristics of the …

Online active learning is a paradigm in machine learning that aims to select the most
informative data points to label from a data stream. The problem of minimizing the cost …

Human activity recognition (HAR) is highly relevant to many real-world domains like safety,
security, and in particular healthcare. The current machine learning technology of HAR is …

Classification of evolving data streams is a challenging task, which is suitably tackled with
online learning approaches. Data is processed instantly requiring the learning machinery to …

Learning from data streams is among the most vital contemporary fields in machine learning
and data mining. Streams pose new challenges to learning systems, due to their volume and …