This paper has been withdrawn by Noor Noor S. Mohammad
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Abstract:Deep ensemble methods often improve predictive performance, yet they suffer from three practical limitations: redundancy among base models that inflates computational cost and degrades conditioning, unstable weighting under multicollinearity, and overfitting in meta-learning pipelines. We propose a regularized meta-learning framework that addresses these challenges through a four-stage pipeline combining redundancy-aware projection, statistical meta-feature augmentation, and cross-validated regularized meta-models (Ridge, Lasso, and ElasticNet). Our multi-metric de-duplication strategy removes near-collinear predictors using correlation and MSE thresholds ($\tau_{\text{corr}}=0.95$), reducing the effective condition number of the meta-design matrix while preserving predictive diversity. Engineered ensemble statistics and interaction terms recover higher-order structure unavailable to raw prediction columns. A final inverse-RMSE blending stage mitigates regularizer-selection variance. On the Playground Series S6E1 benchmark (100K samples, 72 base models), the proposed framework achieves an out-of-fold RMSE of 8.582, improving over simple averaging (8.894) and conventional Ridge stacking (8.627), while matching greedy hill climbing (8.603) with substantially lower runtime (4 times faster). Conditioning analysis shows a 53.7\% reduction in effective matrix condition number after redundancy projection. Comprehensive ablations demonstrate consistent contributions from de-duplication, statistical meta-features, and meta-ensemble blending. These results position regularized meta-learning as a stable and deployment-efficient stacking strategy for high-dimensional ensemble systems.
| Comments: | We have recently encountered author conflicts related to this work and therefore respectfully request the withdrawal of this paper. We believe this step is necessary to address the situation appropriately and maintain academic integrity in the submission |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2602.12469 [cs.LG] |
| (or arXiv:2602.12469v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2602.12469 arXiv-issued DOI via DataCite |
From: Noor Noor S. Mohammad [view email]
[v1]
Thu, 12 Feb 2026 22:55:32 UTC (1,372 KB)
[v2]
Thu, 23 Apr 2026 23:53:56 UTC (1 KB) (withdrawn)
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