The dynamic and unstable nature observed in real world applications influences learning
systems through changes in data, context and resource availability. Data stream mining …

In this paper, we study the problem of learning from concept drifting data streams with noise,
where samples in a data stream may be mislabeled or contain erroneous values. Our …

Mining time-changing data streams is of great interest. The fundamental problems are how
to effectively identify the significant changes and organize new training data to adjust the …

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 …

Online learning involves extracting information from large quantities of data (streams)
usually affected by changes in the distribution (concept drift). A drift detector is a small …

Abstract Due to Big Data and the Internet of Things, machine learning algorithms targeted
specifically to model evolving data streams have gained attention from both academia and …

Nowadays, there are applications in which the data are modeled best not as persistent
tables, but rather as transient data streams. In this article, we discuss the limitations of …

Data stream mining is a fast growing research topic due to the ubiquity of data in several real-
world problems. Given their ephemeral nature, data stream sources are expected to …

This paper proposes a boosting-like method to train a classifier ensemble from data streams.
It naturally adapts to concept drift and allows to quantify the drift in terms of its base learners …

An emerging problem in Data Streams is the detection of concept drift. This problem is
aggravated when the drift is gradual over time. In this work we define a method for detecting …