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 …

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 …

Concept drift primarily refers to an online supervised learning scenario when the relation
between the input data and the target variable changes over time. Assuming a general …

A hands-on approach to tasks and techniques in data stream mining and real-time analytics,
with examples in MOA, a popular freely available open-source software framework. Today …

In the last two decades, machine learning research and practice has focused on batch
learning, usually with small datasets. Nowadays there are applications in which the data are …

Bagging and boosting are well-known ensemble learning methods. They combine multiple
learned base models with the aim of improving generalization performance. To date, they …

We present an ensemble method for concept drift that dynamically creates and removes
weighted experts in response to changes in performance. The method, dynamic weighted …

Online learning algorithms often have to operate in the presence of concept drifts. A recent
study revealed that different diversity levels in an ensemble of learning machines are …

Boosting has become very popular in computer vision, showing impressive performance in
detection and recognition tasks. Mainly off-line training methods have been used, which …

Online learning algorithms often have to operate in the presence of concept drift (ie, the
concepts to be learned can change with time). This paper presents a new categorization for …