Stable lasso for model structure learning of inferential sensor modeling

S. Joe QIN, Yiren LIU

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

1 Citation (Scopus)


In this paper, we propose a stabilization strategy for lasso to use cross-validation (CV) for structure learning. It is known that cross-validation often prefers very small λ that selects an excessively large number of variables, which is also in a less stable region of λ. In this paper, we propose to reduce the heterogeneity of the model structures during the CV step. We first build a series of models using all data with a grid of λ. Then the models of all CV-folds use a revised lasso objective that penalizes deviations from the model structure using all data. Further, we propose a stable selection criterion that uses CV prediction errors jointly with a stability measure to select the most stable model with near minimum CV errors. The proposed strategy is demonstrated using data from an industrial boiler process to predict NOx emissions.

Original languageEnglish
Pages (from-to)228-233
Number of pages6
Issue number7
Early online date15 Sept 2021
Publication statusPublished - 2021
Externally publishedYes
Event19th IFAC Symposium on System Identification (SYSID 2021) - Padova, Italy
Duration: 13 Jul 202116 Jul 2021

Bibliographical note

Financial support for this work from the City University of Hong Kong under Project 9380123: Bridging between Systems Theory and Dynamic Data Learning towards Industrial Intelligence and Industry 4.0 and an NSF-China Regional Joint Key Project for Innovations and Development (U20A20189) is gratefully acknowledged.


  • Inferential sensors
  • Stable cross-validation
  • Stable lasso
  • Statistical machine learning


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