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 …

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 …

Concept drift arises from the uncertainty of data distribution over time and is common in data
stream. While numerous methods have been developed to assist machine learning models …

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 …

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 …

Semi-supervised data stream classification has become a hot research topic, and extensive
research studies have been conducted in this area. There is still lack of enough research in …

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 …