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 …

In todays digital era, a massive amount of streaming data is automatically and continuously
generated. To learn such data streams, many algorithms have been proposed during the …

Data stream mining presents notable challenges in the form of concept drift and evolution.
Existing learning algorithms, typically designed within a supervised learning framework …

In this study, we focus on semi-supervised data stream classification tasks. With the advent
of applications that generate vast streams of data, data stream mining algorithms are …

Data streams are sequences of fast-growing and high-speed data points that typically suffer
from the infinite length, large volume, and specifically unstable data distribution. Ensemble …

High-accurate machine learning (ML) image classifiers cannot guarantee that they will not
fail at operation. Thus, their deployment in safety-critical applications such as autonomous …

Recently, machine learning has achieved considerable success in the field of mechanical
fault diagnosis. Nevertheless, in many real-world applications, the original vibration data …

In this article, we consider the semi-supervised data stream classification problems. Most of
the semi-supervised learning algorithms suffer from a proper selection metric to select from …

Inthis article, wefocus on the classification problem to semi-supervised learning in non-
stationary environment. Semi-supervised learning is a learning task from both labeled and …

Possibilistic fuzzy c-means is a widely used fuzzy clustering algorithm. This algorithm is
capable of handling outlier data points, rendering it a viable option for maintaining a data …