Simplex-Constrained Sparse Bagging: Transitioning from Uniform Priors to Sparse Posteriors in Ensemble Learning
arXiv:2606.13589v1 Announce Type: cross Abstract: We present Simplex-Constrained Sparse Bagging (SCSB), a mathematically rigorous framework for post-training compression and probability calibration of bootstrap-based bagging ensembles. Standard bagging ensembles (such as Random Forests, Bagged SVMs, and Bagged Neural Networks) assign uniform voting power to all constituent estimators. However, this naive uniform prior ignores the varying local competence of base estimators and contributes to model overconfidence. We formulate ensemble pruning and calibration as a joint optimization problem ove
The continuous drive for more efficient and reliable AI models, especially in high-stakes applications, necessitates ongoing research into ensemble methods and model calibration.
This development addresses a fundamental limitation in current ensemble learning, offering a pathway to more accurate and less overconfident AI predictions, which is crucial for deployment in critical systems.
The proposed SCSB framework introduces a mathematically rigorous method for post-training compression and probability calibration in ensemble learning, moving beyond uniform voting assumptions.
- · AI researchers
- · Machine learning engineers
- · Industries relying on critical AI systems
- · Developers of ensemble learning frameworks
- · Systems with high AI overconfidence
- · Inefficient ensemble models
- · Brute-force model stacking approaches
Improved reliability and interpretability of AI models in applications such as autonomous vehicles or medical diagnostics.
Reduced computational overhead for certain AI deployments due to more efficient ensemble compression.
Broader adoption of ensemble methods in production environments as their accuracy and calibration improve, potentially leading to new benchmarks for AI performance.
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Read at arXiv cs.AI