An empirical evaluation of stacked ensembles with different meta-learners in imbalanced classification

Zian, Seng and Abdul Kareem, Sameem and Varathan, Kasturi Dewi (2021) An empirical evaluation of stacked ensembles with different meta-learners in imbalanced classification. IEEE Access, 9. pp. 87434-87452. ISSN 2169-3536, DOI

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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 straightforward as different meta-learners are advocated by different researchers. To investigate and identify a well-performing type of meta-learner in stacked ensemble for imbalanced classification, an experiment consisting of 19 meta-learners was conducted, detailed in this paper. Among the 19 meta-learners of stacked ensembles, a new weighted combination-based meta-learner that maximizes the H-measure during the training of stacked ensemble was first introduced and implemented in the empirical evaluation of this paper. The classification performances of stacked ensembles with 19 different meta-learners were recorded using both the area under the receiver operating characteristic curve (AUC) and H-measure (a metric that overcomes the deficiencies of the AUC). The weighted combination-based meta-learners of stacked ensembles have better classification performances on imbalanced datasets when compared to bagging-based, boosting-based, Decision Trees, Support Vector Machines, Naive Bayes, and Feedforward Neural Network meta-learners. Thus, the adoption of weighted combination-based meta-learners in stacked ensembles is recommended for their better performance on imbalanced datasets. Also, based on the empirical results, we identified better-performing meta-learners (such as the AUC maximizing meta-learner and the H-measure maximizing meta-learner) than the widely adopted meta-learner - Logistic Regression - in imbalanced classification.

Item Type: Article
Funders: Faculty Research Grant of Universiti Malaya (GF011D-2019)
Uncontrolled Keywords: Measurement; Training; Prediction algorithms; Metadata; Boosting; Task analysis; Standards; Class imbalance; H-measure; Imbalanced classification; Meta-learner; Stacked ensemble; Stacking; Super learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms Zaharah Ramly
Date Deposited: 08 Apr 2022 04:42
Last Modified: 08 Apr 2022 04:42

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