AER is a dynamic ensemble method that reports improved balanced accuracy on seven UCI imbalanced datasets and five GMM-generated variants, with a theoretical complexity claim that is not correctly proved.
Combining multiple algorithms in classifier ensembles using generalized mixture functions.Neurocomputing, 313:402–414, 2018
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Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification
AER is a dynamic ensemble method that reports improved balanced accuracy on seven UCI imbalanced datasets and five GMM-generated variants, with a theoretical complexity claim that is not correctly proved.