An Empirical Evaluation of Stacked Ensembles With Different Meta-Learners in Imbalanced Classification

An Empirical Evaluation of Stacked Ensembles With Different Meta-Learners in Imbalanced Classification

The selection of a meta-learner determines the success of a stacked ensemble as the meta-learner is responsible for the final predictions of the stacked ensemble. Unfortunately, in imbalanced classification, selecting an appropriate and well-performing meta-learner of stacked ensemble is not straigh...

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Bibliographic Details
Main Authors:	Seng Zian, Sameem Abdul Kareem, Kasturi Dewi Varathan
Format:	Article
Language:	English
Published:	IEEE 2021-01-01
Series:	IEEE Access
Subjects:	Class imbalance H-measure imbalanced classification meta-learner stacked ensemble stacking
Online Access:	https://ieeexplore.ieee.org/document/9452051/

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