Concept drift describes unforeseeable changes in the underlying distribution of streaming
data overtime. Concept drift research involves the development of methodologies and …

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 …

Incremental and online learning algorithms are more relevant in the data mining context
because of the increasing necessity to process data streams. In this context, the target …

Concept drift techniques aim at learning patterns from data streams that may change over
time. Although such behavior is not usually expected in controlled environments, real-world …

Common statistical prediction models often require and assume stationarity in the data.
However, in many practical applications, changes in the relationship of the response and …

Concept drift refers to a non stationary learning problem over time. The training and the
application data often mismatch in real life problems. In this report we present a context of …

In a non-stationary environment, newly received data may have different knowledge patterns
from the data used to train learning models. As time passes, a learning model's performance …

Online learning regards extracting information from large quantities of data (streams) usually
affected by changes in the distribution (concept drift). Drift detectors are software that …

In many real-world applications, data are often collected in the form of a stream, and thus the
distribution usually changes in nature, which is referred to as concept drift in the literature …

When using statistical models (such as a classifier) in a streaming environment, there is
often a need to detect and adapt to concept drifts to mitigate any deterioration in the model's …