As data streams are gaining prominence in a growing number of emerging application
domains, classification on data streams is becoming an active research area. Currently, the …

Data stream mining has been receiving increased attention due to its presence in a wide
range of applications, such as sensor networks, banking, and telecommunication. One of the …

In many applications of information systems learning algorithms have to act in dynamic
environments where data are collected in the form of transient data streams. Compared to …

Data stream classification is the process of learning supervised models from continuous
labelled examples in the form of an infinite stream that, in most cases, can be read only once …

Ensemble learning methods for evolving data streams are extremely powerful learning
methods since they combine the predictions of a set of classifiers, to improve the …

Ensemble-based methods are among the most widely used techniques for data stream
classification. Their popularity is attributable to their good performance in comparison to …

Designing adaptive classifiers for an evolving data stream is a challenging task due to the
data size and its dynamically changing nature. Combining individual classifiers in an online …

Abstract Analysis of data streams is becoming a key area of data mining research, as the
number of applications demanding such processing increases. Modern information …

This paper presents a novel ensemble learning method based on evolutionary algorithms to
cope with different types of concept drifts in non-stationary data stream classification tasks. In …

Ensemble learning has become a common tool for data stream classification, being able to
handle large volumes of stream data and concept drifting. Previous studies focus on building …