A Novel Hybrid Feature Importance and Feature Interaction Detection
  Framework for Predictive Optimization in Industry 4.0 Applications
Zhipeng Ma Bo Nørregaard Jørgensen Zheng Grace Ma
Advanced machine learning algorithms are increasingly utilized to provide
data-based prediction and decision-making support 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.