Concept drift

In predictive analytics and machine learning, the concept drift means that the statistical properties of the target variable, which the model is… (More)
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Highly Cited
2013
Highly Cited
2013
Learning in nonstationary environments, also known as learning concept drift, is concerned with learning from data whose… (More)
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Highly Cited
2012
Highly Cited
2012
0167-8655/$ see front matter 2011 Published by doi:10.1016/j.patrec.2011.08.019 ⇑ Corresponding author. Tel.: +44 (0) 2075940990… (More)
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Highly Cited
2011
Highly Cited
2011
We introduce an ensemble of classifiers-based approach for incremental learning of concept drift, characterized by nonstationary… (More)
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Highly Cited
2008
Highly Cited
2008
In the real world concepts are often not stable but change with time. A typical example of this in the biomedical context is… (More)
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Highly Cited
2007
Highly Cited
2007
We present an ensemble method for concept drift that dynamically creates and removes weighted experts in response to changes in… (More)
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Highly Cited
2004
Highly Cited
2004
Most of the work in machine learning assume that examples are generated at random according to some stationary probability… (More)
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Highly Cited
2004
Highly Cited
2004
In this paper we present a multiple window incremental learning algorithm that distinguishes between virtual concept drift and… (More)
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Highly Cited
2003
Highly Cited
2003
Concept drift occurs when a target concept changes over time. We present a new method for learning shifting target concepts… (More)
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Highly Cited
2000
Highly Cited
2000
For many learning tasks where data is collected over an extended period of time, its underlying distribution is likely to change… (More)
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Highly Cited
1996
Highly Cited
1996
On-line learning in domains where the target concept depends on some hidden context poses serious problems. A changing context… (More)
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