Author(s)

WanJun Cai^1*, JiangYi Le², Rui Gu³, Hang Zhao⁴, SiYu Tang³

Affiliation(s)

¹School of Artificial Intelligence, Shanghai University of Electric Power, Shanghai 201306, China.

²College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, China.

³New Energy Science and Engineering, Shanghai University of Electric Power, Shanghai 201306, China.

⁴School of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China.

Corresponding Author

WanJun Cai

ABSTRACT

The challenge of capturing high-dimensional interactions and ensuring stable predictions in complex engineering systems, characterized by rapidly growing heterogeneous data, limits traditional models. To address this, we introduce FEAST (Feature Engineering and Advanced Stacking Framework), whose core innovation lies in its integrated approach: (1) Hierarchical feature optimization using K-Means++ clustering and adaptive correlation to significantly reduce redundancy and enhance feature discrimination; (2) An advanced stacked ensemble leveraging diverse base learners (MLR, XGBoost, LightGBM, CatBoost, GBDT) fused via Shapley-weighted combination and a regularized (Elastic Net) meta-learner to robustly capture complex patterns; (3) An efficient Monte Carlo Dropout-based uncertainty quantification module providing reliable confidence intervals for risk-aware decisions. Comprehensive experiments demonstrate FEAST's superiority: it achieves 12.7% higher prediction accuracy (p<0.01), 41% lower cross-validation error variability, and a 92.3% confidence interval coverage rate, significantly outperforming baseline models in complex engineering prediction tasks.

KEYWORDS

Feature engineering; Stacked ensemble learning; Uncertainty quantification; High-dimensional feature optimization; Complex system prediction

CITE THIS PAPER

WanJun Cai, JiangYi Le, Rui Gu, Hang Zhao, SiYu Tang. Feast: feature engineering and stacking framework for complex system prediction. World Journal of Engineering Research. 2026, 4(1): 37-42. DOI: https://doi.org/10.61784/wjer3076.

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