The content introduces a novel approach to optimize predictive models by combining feature importance and interaction detection. The proposed framework aims to improve prediction accuracy by removing unnecessary features and encoding interactions. Experimental results show significant enhancements in R2 scores and reductions in root mean square error, demonstrating the effectiveness of the approach.
The article discusses the importance of data pre-processing in Industry 4.0 applications, emphasizing the need for feature selection to enhance data analysis effectiveness. It highlights the significance of identifying important variables for accurate predictions and leveraging feature interactions to improve outcomes.
Various algorithms for detecting feature importance and interactions are explored, including LIME for local interpretability and NID for neural interaction detection. These methods are applied to refine predictions of electricity consumption in foundry processing, resulting in notable performance improvements.
The methodology section details a general pipeline for implementing the hybrid framework, involving feature reconstruction, interaction embedding, and feature selection stages. Parameter setting suggestions are provided based on experimental findings to optimize prediction performance effectively.
Experimental results demonstrate the robustness of the proposed framework in optimizing both R2 scores and RMSE across different prediction models. The discussion delves into how LIME provides interpretable explanations while NID offers unique insights into variable relationships, enhancing strategic planning capabilities.
In conclusion, the hybrid framework not only optimizes predictive models but also serves as an explanatory tool for industrial stakeholders. Future work could focus on further enhancing feature selection processes using intelligent algorithms and refining interaction feature generation methods.
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