Comprehensive model optimization in pulp quality prediction: a machine learning approach
Bibliographic record
Abstract
Feature selection in machine learning is of great interest since it is reckoned as creating more efficient predictive models in several engineering domains. It is even of special importance in the pulp and paper transformation industry as the knowledge of this particular process is generally very limited. In this paper, we first compared the performance of rule-based genetic algorithm and that of adaptive neuro-fuzzy inference system; the latter is found to be more precise in predicting the pulp quality. We then combined several data mining algorithms such as genetic algorithm-partial least square regression, along with other statistical methods, to explore the relevance of all the potential variables that could be used to predict the pulp ISO brightness, an important property that is usually linked to model performance and hence pulp quality prediction. A few highly relevant variables are thereby determined, and the full set of 79 variables obtained from a Chip Management System was trimmed down to an optimized combination of 3 inputs depending on their relevancy. Peroxide charge (P), average luminance (L) and hue (H) were chosen as the optimal subset to describe the ISO brightness of the pulp and the model was simplified without losing much of its accuracy. Finally, we derived the numbers of membership functions for each variable to further refine the fuzzy logic-based prediction model. The error then reached 2.18%. The loss on accuracy was compensated by adjusting to the fittest membership function numbers
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".