Advanced machine learning algorithms are increasingly utilized to provide data-based support for prediction and decision-making in Industry 4.0. However, the prediction accuracy achieved by the existing models is insufficient to warrant practical implementation in real-world applications. This is because not all features present in real-world datasets possess a direct relevance to the predictive analysis being conducted. Consequently, the careful incorporation of select features has the potential to yield a substantial positive impact on the outcome. To address the research gap, this paper proposes a novel hybrid framework that combines the feature importance detector - local interpretable model-agnostic explanations (LIME) and the feature interaction detector - neural interaction detection (NID), to improve prediction accuracy. By applying the proposed framework, unnecessary features can be eliminated, and interactions are encoded to generate a more conducive dataset for predictive purposes. Subsequently, the proposed model is deployed to refine the prediction of electricity consumption in foundry processing. The experimental outcomes reveal an augmentation of up to 9.56% in the R2 score, and a diminution of up to 24.05% in the root mean square error.
|Titel||IECON 2023–49th Annual Conference of the IEEE Industrial Electronics Society|
|Status||Accepteret/In press - 13. jul. 2023|
|Begivenhed||IECON 2023–49th Annual Conference of the IEEE Industrial Electronics Society - Marina Bay Sands Expo and Convention Centre, Singapore, Singapore|
Varighed: 16. okt. 2023 → 19. okt. 2023
|Konference||IECON 2023–49th Annual Conference of the IEEE Industrial Electronics Society|
|Lokation||Marina Bay Sands Expo and Convention Centre|
|Periode||16/10/2023 → 19/10/2023|