Research map / Label-efficient and robust learning / Recognition and long-tail
An urn model for majority voting in classification ensembles
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| non-uniform OOB-estimated vote prior (HYPER) instead of uniform prior (SIBA) for dynamic halting | Random Forest dynamic pruning · 20 benchmark datasets (UCI and synthetic) | disagreement % with full ensemble (target 1%) | Helped |
| non-uniform OOB-estimated vote prior (HYPER) instead of uniform prior (SIBA) | Random Forest dynamic pruning · Mushroom, Breast, Votes, Wine, etc. (number of queried trees) | average number of queried trees | Helped |
| non-uniform OOB-estimated vote prior (HYPER) instead of uniform prior (SIBA) | Random Forest dynamic pruning · 20 benchmark datasets, test error | test error rate | Mixed |
| dynamic halting with uniform prior (SIBA) or non-uniform prior (HYPER) instead of full 101-tree ensemble | Random Forest · Mushroom | speed-up rate | Helped |
Closest papers by meaning
- Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound (NeurIPS 2021 · similarity 0.88)
- Second Order PAC-Bayesian Bounds for the Weighted Majority Vote (NeurIPS 2020 · similarity 0.87)
- How many classifiers do we need? (NeurIPS 2024 · similarity 0.87)
- Exact power indices for plurality-voting ensembles (NeurIPS 2026 · similarity 0.86)
- The Many Faces of Optimal Weak-to-Strong Learning (NeurIPS 2024 · similarity 0.82)
- Class-Attribute Priors: Adapting Optimization to Heterogeneity and Fairness Objective (AAAI 2024 · similarity 0.82)
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