Learning under concept drift: A review
Concept drift describes unforeseeable changes in the underlying distribution of streaming
data overtime. Concept drift research involves the development of methodologies and …
data overtime. Concept drift research involves the development of methodologies and …
Ensemble learning for data stream analysis: A survey
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 …
environments where data are collected in the form of transient data streams. Compared to …
Online and non-parametric drift detection methods based on Hoeffding's bounds
I Frias-Blanco, J del Campo-Ávila… - … on Knowledge and …, 2014 - ieeexplore.ieee.org
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 …
because of the increasing necessity to process data streams. In this context, the target …
An overview on concept drift learning
AS Iwashita, JP Papa - IEEE access, 2018 - ieeexplore.ieee.org
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 …
time. Although such behavior is not usually expected in controlled environments, real-world …
Concept drift detection for streaming data
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 …
However, in many practical applications, changes in the relationship of the response and …
Learning under concept drift: an overview
I Žliobaitė - arXiv preprint arXiv:1010.4784, 2010 - arxiv.org
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 …
application data often mismatch in real life problems. In this report we present a context of …
Accumulating regional density dissimilarity for concept drift detection in data streams
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 …
from the data used to train learning models. As time passes, a learning model's performance …
Wilcoxon rank sum test drift detector
RSM de Barros, JIG Hidalgo, DR de Lima Cabral - Neurocomputing, 2018 - Elsevier
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 …
affected by changes in the distribution (concept drift). Drift detectors are software that …
[HTML][HTML] Handling concept drift via model reuse
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 …
distribution usually changes in nature, which is referred to as concept drift in the literature …
Concept drift detection with hierarchical hypothesis testing
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 …
often a need to detect and adapt to concept drifts to mitigate any deterioration in the model's …