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Concept drift

In predictive analytics and machine learning, the concept drift means that the statistical properties of the target variable, which the model is… 
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Related topics

Related topics

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Broader (2)

Data miningMachine learning
Data stream miningECML PKDDIncremental decision treeMOA

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2016
2016
Active Learning Classification of Drifted Streaming Data
2014
2014
Hyper-ellipsoidal clustering technique for evolving data stream
2012
2012
Naive random subspace ensemble with linear classifiers for real-time classification of fMRI data
2010
2010
Cost-sensitive Boosting for Concept Drift
Concept drift is a phenomenon typically experienced when data distributions change continuously over a period of time. In this… 
2009
2009
Metric-based stochastic conceptual clustering for ontologies
2008
2008
Learning and Detecting Concept Drift
The volume of data that humans create has increased explosively as information science and technology have evolved. Therefore… 
2008
2008
Drift pumice in the central Indian Ocean Basin: Geochemical evidence
Highly Cited
2007
Highly Cited
2007
The impact of fine sediment accumulation on benthic macroinvertebrates: implications for river management
Fine sediment accumulation (particle size: <4mm) is a major cause of degradation to instream habitats and ecological condition in… 
2007
2007
An evaluation of Naive Bayes variants in content-based learning for spam filtering
We describe an in-depth analysis of spam-filtering performance of a simple Naive Bayes learner and two extended variants. A set… 
Review
1998
Review
1998
Evaluating Usefulness for Dynamic Classification
This paper develops the concept of usefulness in the context of supervised learning. We argue that usefulness can be used to… 
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