Mining concept-drifting data streams using ensemble classifiers
Citations
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13,246 citations
2,591 citations
2,374 citations
Cites background or methods from "Mining concept-drifting data stream..."
...2004], [Wang et al. 2003] [Bifet and Gavalda 2006], [Kuncheva and Zliobaite 2009] Model Management...
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...…Model Adaptation Model Specific [Hulten et al. 2001], [Gama et al. 2006], [Harries et al. 1998] Model Independent [Wald 1947], [Gama et al. 2004], [Wang et al. 2003] [Bifet and Gavalda 2006], [Kuncheva and Zliobaite 2009] Model Management Single Model [Hulten et al. 2001], [Gama et al. 2006],…...
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1,790 citations
Cites background from "Mining concept-drifting data stream..."
...This is referred to as data evolution, dynamic stream, time-changing data, or concept-drifting data [3, 69, 78, 157]....
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...[78] and Aggarwal [3, 4] are representative of the first track and much work [69, 157, 49, 51] belongs to the second track....
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...introduced a framework [157] that can also deal with time-changing streams by using weighted classifier ensembles....
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1,654 citations
Cites methods from "Mining concept-drifting data stream..."
...Such learning algorithms include decision trees [37], SVMs [18], instance-based learning [1], etc....
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References
21,674 citations
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13,246 citations
"Mining concept-drifting data stream..." refers methods in this paper
...5 [24], the RIPPER rule learner [6], and the Naive Bayesian method [23]....
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8,046 citations
7,601 citations
"Mining concept-drifting data stream..." refers background or methods in this paper
...The popular approaches to creating ensembles include changing the instances used for training through techniques such as Bagging [3], Boosting [ 13 ], and pasting [4]....
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...First, classifier ensembles offer a significant improvement in prediction accuracy [ 13 , 28]....
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