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SELFVarBL: a Stacking-based Ensemble Learning Framework with Variable numbers of Base-Learners
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DOI：10.1007/s12065-025-01085-z.png)
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Abstract
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Stacking is one of the most popular ensemble learning technique that combines the predictions of multiple base-learners (BLs) using a meta-learner to improve the overall performance of the model. To build a stacking-based ensemble, most of the existing works select top-k-performing models as the base-learners without considering the computational resource requirement of the system. This work presents a Stacking-based Ensemble Learning Framework with a Variable number of Base-Learners (SELFVarBL), which is capable of finding the trade-off between computational resource requirements and system performance by automatically selecting a suitable number and combinations of base-learners. Additionally, the proposed framework does not require excessive computational resources to achieve the optimal solution. The proposed framework is validated using three case studies with diversify datasets: the agriculture domain, the audio data analysis domain, and the self-generated synthetic datasets. The experimental results show the effectiveness of the proposed framework.
Keywords:
SELFVarBL
Stacking-based ensemble
Ensemble learning
Machine learning
Journal
IF:
2.6
Papers:
116
Citations:
2.0K
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