正则化元学习以提升泛化能力
Regularized Meta-Learning for Improved Generalization
AI总结：
本文提出一种正则化元学习框架，通过四阶段流程解决深度集成方法的冗余、多共线性不稳定和元学习过拟合问题，提升预测性能并降低运行时间。
摘要：
深度集成方法常能提升预测性能，但面临三个实际限制：基模型冗余导致计算成本增加和条件恶化，多共线性下的权重不稳定，以及元学习流程中的过拟合。我们提出一种正则化元学习框架，通过四阶段流程结合冗余感知投影、统计元特征增强和交叉验证正则化元模型（Ridge、Lasso和ElasticNet）。我们的多指标去重策略利用相关性和MSE阈值（τ_corr=0.95）去除近共线性预测符，降低元设计矩阵的有效条件数同时保持预测多样性。工程化的集成统计和交互项恢复无法通过原始预测列获取的高阶结构。最终的逆RMSE融合阶段缓解正则器选择的方差。在Playground Series S6E1基准（10万样本，72个基模型）上，所提框架的出折RMSE为8.582，优于简单平均（8.894）和传统Ridge堆叠（8.627），同时匹配贪心山峰爬升（8.603）且运行时间低4倍。条件分析显示冗余投影后有效矩阵条件数减少53.7%。全面的消融实验显示去重、统计元特征和元堆叠融合的贡献一致。这些结果将正则化元学习定位为高维集成系统稳定且部署高效的堆叠策略。
英文摘要：
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 ($τ_{\text{corr}}=0.95$), 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
