In recent years, the availability of time series streaming information has been growing
enormously. Learning from real-time data has been receiving increasingly more attention …

Unlabelled data appear in many domains and are particularly relevant to streaming
applications, where even though data is abundant, labelled data is rare. To address the …

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 …

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 …

The present paper focuses on semi-supervised classification problems. Semi-supervised
learning is a learning task through both labeled and unlabeled samples. One of the main …

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 the context of streaming data, learning algorithms often need to confront several unique
challenges, such as concept drift, label scarcity, and high dimensionality. Several concept …

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 …

Data stream classification is of great significance to numerous real-world scenarios.
Nevertheless, the prevalent data stream classification techniques are influenced by concept …

The Internet provides a platform for sharing services, and web service brokers help users to
choose the suitable service among similar services based on ranking. The quality of service …