A stable Lasso algorithm for inferential sensor structure learning and parameter estimation

S. Joe QIN*, Yiren LIU

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

7 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)70-82
Number of pages13
JournalJournal of Process Control
Volume107
Early online date26 Oct 2021
DOIs
Publication statusPublished - Nov 2021
Externally publishedYes

Bibliographical note

Financial support for this work from the City University of Hong Kong under Project 9380123 and an NSF-China Regional Joint Key Project for Innovations and Development (U20A20189) is gratefully acknowledged.

Keywords

  • Inferential sensors
  • Stable cross-validation
  • Stable Lasso
  • Statistical machine learning
  • Variable selection

Fingerprint

Dive into the research topics of 'A stable Lasso algorithm for inferential sensor structure learning and parameter estimation'. Together they form a unique fingerprint.

Cite this