Among the many issues related to data stream applications, those involved in predictive
tasks such as classification and regression, play a significant role in Machine Learning (ML) …

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 …

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 …

Ensemble methods are a popular choice for learning from evolving data streams. This
popularity is due to (i) the ability to simulate simple, yet, successful ensemble learning …

Random forests is currently one of the most used machine learning algorithms in the non-
streaming (batch) setting. This preference is attributable to its high learning performance and …

Ensemble learning has been widely applied to both batch data classification and streaming
data classification. For the latter setting, most existing ensemble systems are homogenous …

Most of studies on streaming data classification are based on the assumption that data can
be fully labeled. However, in real-life applications, it is impractical and time-consuming to …

Data stream classification techniques have been playing an important role in big data
analytics recently due to their diverse applications (eg fraud and intrusion detection …

With the advance in both hardware and software technologies, automated data generation
and storage has become faster than ever. Such data is referred to as data streams …

In this digital era we are surrounded by social media applications and the hardware devices
(such as sensorsetc) which are pouring data at an astonishing rate. This incoming data from …