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 learning to classify streaming data, obtaining true labels may require major effort and may
incur excessive cost. Active learning focuses on carefully selecting as few labeled instances …

Studying the relationship between public sentiment and stock prices has been the focus of
several studies. This paper analyzes whether the sentiment expressed in Twitter feeds …

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 todays digital era, a massive amount of streaming data is automatically and continuously
generated. To learn such data streams, many algorithms have been proposed during the …

Data stream classification poses many challenges to the data mining community. In this
paper, we address four such major challenges, namely, infinite length, concept-drift, concept …

In this paper, we propose a new research problem on active learning from data streams,
where data volumes grow continuously, and labeling all data is considered expensive and …

The problem of data stream classification is challenging because of many practical aspects
associated with efficient processing and temporal behavior of the stream. Two such well …

Streaming data analysis potentially represents a significant shift in emphasis from schemes
historically pursued for offline (batch) approaches to the classification task. In particular, a …

In streaming data classification, most of the existing methods assume that all arrived
evolving data are completely labeled. One challenge is that some applications where only …