Feature selection and regularization of interpretable soft sensors using evolutionary multi-objective optimization design procedures

Soft sensors are mathematical models that estimate hard-to-measure variables given easy-to-measure ones. This field of study has given the industry a valuable tool to enable a better control of different plants and processes. With such models, quality indicators and other variables that usually demand costly or slow sensors can be predicted in real time. However, one important factor for application in industry is model interpretability. Moreover, regression models have an inherent trade-off between bias and variance, which is not considered by usual learning algorithms. Therefore, this work proposes a novel multi-objective least squares based on evolutionary feature selection and regularization, which incorporates feature selection and regularization, as well as the search for a better trade-off between bias and variance, using evolutionary multi-objective optimization algorithms. Experiments on two famous soft sensor benchmark problems, the debutanizer column and the sulfur recovery unit, indicate that the proposed algorithm can train more robust interpretable linear models than the commonly used partial least squares and least absolute shrinkage and selection operator.

Automated summary

Soft sensors are mathematical models that estimate hard-to-measure variables given easy-to-measure ones, providing a valuable tool for the industry. The proposed multi-objective least squares method can train more robust and interpretable linear models compared to commonly used methods.