A Novel Deep Ensemble Learning Framework for Classifying Imbalanced Data Stream
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-16-3945-6_60
Reference14 articles.
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2. Klikowski, J., Woźniak, M.: Multi sampling random subspace ensemble for imbalanced data stream classification. In: International Conference on Computer Recognition Systems, pp. 360–369. Springer, Cham (2019)
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4. Zhang, Y., Liu, B., Cai, J., Zhang, S.: Ensemble weighted extreme learning machine for imbalanced data classification based on differential evolution. Neural Comput. Appl. 28(1), 259–267 (2017)
5. Yijing, L., Haixiang, G., Xiao, L., Yanan, L., Jinling, L.: Adapted ensemble classification algorithm based on multiple classifier system and feature selection for classifying multi-class imbalanced data. Knowl. Based Syst. 94, 88–104 (2016)
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