Model stacking to improve prediction and variable importance robustness for soft sensor development
This paper presents an interpretable ensemble modelling method, in which the predictions of several individual base learners are combined together through Stacked generalisation, which makes use of a secondary layer model, or so called meta-learner, that is trained on the output cross-validation pre...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | Digital Chemical Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772508122000254 |
| _version_ | 1818202641826578432 |
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| author | Maxwell Barton Barry Lennox |
| author_facet | Maxwell Barton Barry Lennox |
| author_sort | Maxwell Barton |
| collection | DOAJ |
| description | This paper presents an interpretable ensemble modelling method, in which the predictions of several individual base learners are combined together through Stacked generalisation, which makes use of a secondary layer model, or so called meta-learner, that is trained on the output cross-validation predictions of each base learner. To provide interpretability, the permutation variable importance (PVI) is computed on the ensemble, wherein variables are randomly shuffled and the reduction in predictive performance for the ensemble is calculated for each variable. This is a novel contribution, as no previous attempts have been made in the soft sensor literature to investigate the interpretability of ensemble models that use heterogeneous base learners. The Stacked ensemble model also avoids model selection, which is the process of choosing among many candidate models. Model selection is often based on cross-validation, which is not guaranteed to select the best model in terms of true generalisation performance on the test set. Instead, the proposed method combines multiple models instead of choosing a singular model, avoiding the need for model selection. The efficacy of the proposed methodology in terms of both variable importance and predictive performance is shown on a synthetic dataset, in which the variable importance is already known, and an industrial dataset of a refinery process provided by Dow. For the synthetic dataset, it is shown that the proposed method chooses the correct casual variables, whereas the in-built variable importance provided by the individual models, namely Partial least squares, Lasso, Random forests & XGBoost, can give increased importance to non-causal, randomly generated variables. For the industrial study, the combined ensemble is shown to outperform all individual base models in terms of predictive performance, whilst also providing a new perspective in terms of variable importance compared to previous studies. |
| first_indexed | 2024-12-12T03:12:41Z |
| format | Article |
| id | doaj.art-029142e3917141f6b9ad21debac5f1ab |
| institution | Directory Open Access Journal |
| issn | 2772-5081 |
| language | English |
| last_indexed | 2024-12-12T03:12:41Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Digital Chemical Engineering |
| spelling | doaj.art-029142e3917141f6b9ad21debac5f1ab2022-12-22T00:40:21ZengElsevierDigital Chemical Engineering2772-50812022-06-013100034Model stacking to improve prediction and variable importance robustness for soft sensor developmentMaxwell Barton0Barry Lennox1Corresponding author.; University of Manchester, Manchester, Greater Manchester, United KingdomUniversity of Manchester, Manchester, Greater Manchester, United KingdomThis paper presents an interpretable ensemble modelling method, in which the predictions of several individual base learners are combined together through Stacked generalisation, which makes use of a secondary layer model, or so called meta-learner, that is trained on the output cross-validation predictions of each base learner. To provide interpretability, the permutation variable importance (PVI) is computed on the ensemble, wherein variables are randomly shuffled and the reduction in predictive performance for the ensemble is calculated for each variable. This is a novel contribution, as no previous attempts have been made in the soft sensor literature to investigate the interpretability of ensemble models that use heterogeneous base learners. The Stacked ensemble model also avoids model selection, which is the process of choosing among many candidate models. Model selection is often based on cross-validation, which is not guaranteed to select the best model in terms of true generalisation performance on the test set. Instead, the proposed method combines multiple models instead of choosing a singular model, avoiding the need for model selection. The efficacy of the proposed methodology in terms of both variable importance and predictive performance is shown on a synthetic dataset, in which the variable importance is already known, and an industrial dataset of a refinery process provided by Dow. For the synthetic dataset, it is shown that the proposed method chooses the correct casual variables, whereas the in-built variable importance provided by the individual models, namely Partial least squares, Lasso, Random forests & XGBoost, can give increased importance to non-causal, randomly generated variables. For the industrial study, the combined ensemble is shown to outperform all individual base models in terms of predictive performance, whilst also providing a new perspective in terms of variable importance compared to previous studies.http://www.sciencedirect.com/science/article/pii/S2772508122000254Machine learningSoft sensorModel interpretability |
| spellingShingle | Maxwell Barton Barry Lennox Model stacking to improve prediction and variable importance robustness for soft sensor development Digital Chemical Engineering Machine learning Soft sensor Model interpretability |
| title | Model stacking to improve prediction and variable importance robustness for soft sensor development |
| title_full | Model stacking to improve prediction and variable importance robustness for soft sensor development |
| title_fullStr | Model stacking to improve prediction and variable importance robustness for soft sensor development |
| title_full_unstemmed | Model stacking to improve prediction and variable importance robustness for soft sensor development |
| title_short | Model stacking to improve prediction and variable importance robustness for soft sensor development |
| title_sort | model stacking to improve prediction and variable importance robustness for soft sensor development |
| topic | Machine learning Soft sensor Model interpretability |
| url | http://www.sciencedirect.com/science/article/pii/S2772508122000254 |
| work_keys_str_mv | AT maxwellbarton modelstackingtoimprovepredictionandvariableimportancerobustnessforsoftsensordevelopment AT barrylennox modelstackingtoimprovepredictionandvariableimportancerobustnessforsoftsensordevelopment |