Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Imbalance ensemble classification is one of the most essential and practical strategies for improving decision performance in data analysis. There is a growing body of literature about ensemble techniques for imbalance learning in recent years, the various extensions of imbalanced classification met...

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Bibliographic Details
Main Authors: Dongxue Zhao, Xin Wang, Yashuang Mu, Lidong Wang
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Entropy
Subjects:
dynamic selection
ensemble classification
imbalanced data classification
multi-class classification
Online Access:https://www.mdpi.com/1099-4300/23/7/822

Internet

https://www.mdpi.com/1099-4300/23/7/822

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