A stable Lasso algorithm for inferential sensor structure learning and parameter estimation. (November 2021) - Qin, S. Joe; Liu, Yiren
Abstract: Although the Lasso method has been popular for variable selection in regression modeling, it has been known to yield very different model structures with minor perturbations of the training data. A consequence is that, when cross-validation (CV) is used to determine the hyperparameter λ, seemingly heterogeneous model structures among the CV-folds are resulted for the same λ . In this paper, we propose a new stable Lasso method for model structure learning of static and dynamic models. We begin with building consensus Lasso models with a grid of λ values using all training data. Then the CV-fold models are optimized to conform with the consensus model structures with a modified Lasso objective. In addition, we propose a stable criterion that uses CV errors jointly with a stability measure to select the most stable model with near minimum CV errors. The proposed method is applied to inferential modeling of a chemical plant at DOW Chemical and dynamic modeling of an industrial boiler. Highlights: A stable Lasso algorithm is proposed to select variables for inferential sensor modeling. The algorithm enhances consistent structures in the cross-validation step. A stable selection criterion of MSE jointly the Jaccard stability measure is proposed. Successful case studies are presented on inferential modeling of two industrial processes.