Mutual information and attention-based variable selection for soft sensing of industrial processes

This study introduces a novel method called mutual information (MI) and attention-based variable selection (MAVS) to address the challenges of irrelevant and redundant variables in industrial process soft sensing while providing interpretability in variable contribution analysis. First, irrelevant variables are eliminated based on low MI values with the quality variable. Second, attention scores are used to remove redundant variables, and the false discovery rate is used to determine the number of beneficial variables. Finally, this work provides an interpretable and accurate contribution of the selected variables by using kernelSHAP, a kernel-based Shapley analysis. Unlike traditional approaches, MAVS integrates MI with attention mechanisms to optimize variable selection dynamically and adaptively. MAVS obtains stronger robustness and higher accuracy than the existing state-of-the-art models through optimal variable selection. The former also obtains better superior generalization than the latter through adaptive adjustment of attention weights. The superiority of MAVS is demonstrated using two real-world datasets and one simulated dataset.