AWhat changed
Builds heterogeneous multilayer stack ensembles using original features and out-of-fold model predictions, repeated bagging and weighted ensemble selection for tabular learning.
Scientific scope
Builds heterogeneous multilayer stack ensembles using original features and out-of-fold model predictions, repeated bagging and weighted ensemble selection for tabular learning. Bagging, stacking, base learners and ensemble selection are inherited. The source notes early-stopping-related leakage can subtly overfit out-of-fold predictions; empirical benchmark/competition comparisons do not guarantee universal superiority. First two authors contribute equally.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Bagging, stacking, base learners and ensemble selection are inherited. The source notes early-stopping-related leakage can subtly overfit out-of-fold predictions; empirical benchmark/competition comparisons do not guarantee universal superiority. First two authors contribute equally.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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