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Classifying Imbalanced Data Streams via Dynamic Feature Group Weighting with Importance Sampling.

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第一作者： Ke,Wu

第一单位： Department of Computer Science, Xavier University of Louisiana.

作者： Ke,Wu ^[1] ; Andrea,Edwards ^[1] ; Wei,Fan ^[2] ; Jing,Gao ^[3] ; Kun,Zhang ^[1]

作者单位： Department of Computer Science, Xavier University of Louisiana. ^[1] Huawei Noah Ark's Lab, david.fanwei@huawei.com. ^[2] Department of Computer Science and Engineering, University at Buffalo, jing@buffalo.edu. ^[3]

关键词 Class imbalance Data stream classification Ensemble weighting Feature group ensemble Importance sampling

DOI 10.1137/1.9781611973440.83

PMID 25568835

发布时间 2020-09-29

基金项目

G12 MD007595/MD/NIMHD NIH HHS/United States

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Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining

2014年2014卷

722-730页

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Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining

2014年2014卷

722-730页

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Classifying Imbalanced Data Streams via Dynamic Feature Group Weighting with Importance Sampling.

Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining

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Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining

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Classifying Imbalanced Data Streams via Dynamic Feature Group Weighting with Importance Sampling.

Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining

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Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining

相关期刊

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