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 …

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 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 …

Data stream classification plays an important role in modern data analysis, where data
arrives in a stream and needs to be mined in real time. In the data stream setting the …

Data stream classification poses many challenges, most of which are not addressed by the
state-of-the-art. We present DXMiner, which addresses four major challenges to data stream …

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 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 …

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 …

Streaming data are increasingly present in real-world applications such as sensor
measurements, satellite data feed, stock market, and financial data. The main characteristics …

One versus all (OVA) decision trees learn k individual binary classifiers, each one to
distinguish the instances of a single class from the instances of all other classes. Thus OVA …