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 …

Developing effective and efficient data stream classifiers is challenging for the machine
learning community because of the dynamic nature of data streams. As a result, many data …

Most approaches to classifying data streams either divide the stream into fixed-size chunks
or use gradual forgetting. Due to evolving nature of data streams, finding a proper size or …

An increasing number of real-world applications are associated with streaming data drawn
from drifting and nonstationary distributions that change over time. These applications …

In practical applications, data stream classification faces significant challenges, such as high
cost of labeling instances and potential concept drifting. We present a new online active …

To decide if an update to a data stream classifier is necessary, existing sliding window
based techniques monitor classifier performance on recent instances. If there is a significant …

In massive data analysis, data usually come in streams. In the last years, several studies
have investigated novelty detection in these data streams. Different approaches have been …

With an increase in targeted attacks such as advanced persistent threats (APTs), enterprise
system defenders require comprehensive frameworks that allow them to collaborate and …

In learning to classify streaming data, obtaining the true labels may require major effort and
may incur excessive cost. Active learning focuses on learning an accurate model with as few …

Data labeling is an expensive and time-consuming task. Choosing which labels to use is
increasingly becoming important. In the active learning setting, a classifier is trained by …