Generalization and comparative studies of similarity measures for Just-in-Time modeling
Bibliographic record
Abstract
Just-in-Time (JIT) modeling has become one of the most effective data analytic approaches for nonlinear time-varying process modeling. Locally weighted partial least squares method (LW-PLS) is the most representative of the JIT modeling methods. It has been widely applied to the development of the virtual sensors that can cope with abrupt changes in process characteristic as well as nonlinearity. LW-PLS has been investigated and applied successfully in various industrial processes. The accuracy of its prediction performance is however strongly dependent on how the similarity between data is determined. Usually, the Euclidean distance or the Mahalanobis distance between input data is used to determine the similarity, but they have a clear limitation, that is, they do not take into account of the relationship between the input variables and output variables when selecting data for modeling. Other advanced methods to determine the similarities with the considerations of the relationship between input and output have also been proposed in the literature in a specific form. This work further investigates the properties of these advanced methods and proposes extensions as well as points out opportunities for further research. The comparison and effectiveness of these methods along with their generalizations are demonstrated through a numerical example and an industrial application example.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".