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 data stream mining, the emergence of new patterns or a pattern ceasing to exist is called
concept drift. Concept drift makes the learning process complicated because of the …

One recent area of interest in computer science is data stream management and processing.
By 'data stream', we refer to continuous and rapidly generated packages of data. Specific …

Although the issues of concept drift and class imbalance have been studied separately, the
joint problem is underexplored even though it has received increasing attention. Concept …

The traditional pattern recognition method assumes that the model that is used does not
depend on data timing. This assumption is correct for several practical issues but it is not …

Stream-mining approach is defined as a set of cutting-edge techniques designed to process
streams of data in real time, in order to extract knowledge. In the particular case of …

Prediction in streaming data is an important activity in the modern society. Two major
challenges posed by data streams are (1) the data may grow without limit so that it is difficult …

In this research we address the problem of capturing recurring concepts in a data stream
environment. Recurrence capture enables the re-use of previously learned classifiers …

The problem addressed in this study concerns mining data streams with concept drift. The
goal of the article is to propose and validate a new approach to mining data streams with …

Learning from streaming data is challenging as the distribution of incoming data may
change over time, a phenomenon known as concept drift. The predictive patterns, or …