EEE, Remediating the failure of machine learning models via a network-based optimization patch

• DOI: 10.48550/arxiv.2304.11321
• Publication Date: 2023-01-01
• Authors: Ruiyuan Kang; Dimitrios C. Kyri...; Panos Liatsis
• Keywords: Ensemble forecasting; Ensemble Learning

A network-based optimization approach, EEE, is proposed for the purpose of providing validation-viable state estimations to remediate failure pretrained models. To improve efficiency and convergence, most important metrics in context this research, we follow a three-faceted approach based on error from validation process. Firstly, information content by designing module acquire high-dimensional information. Next, reduce uncertainty transfer employing an ensemble estimators, which only learn implicit errors, use Constrained Ensemble Exploration collect high-value data. Finally, effectiveness utilization improved using search determine prosperous state. The benefits framework are demonstrated four real-world engineering problems with diverse dimensions. It shown that EEE either as competitive or outperforms popular methods, terms convergence.