Ensemble Learning with Highly Variable Class-Based Performance
This paper proposes a novel model-agnostic method for weighting the outputs of base classifiers in machine learning (ML) ensembles. Our approach uses class-based weight coefficients assigned to every output class in each learner in the ensemble. This is particularly useful when the base classifiers...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-09-01
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| Series: | Machine Learning and Knowledge Extraction |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-4990/6/4/106 |
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