Three-Way Group Decision-Making With Personalized Numerical Scale of Comparative Linguistic Expression: An Application to Traditional Chinese Medicine
Bibliographic record
Abstract
In most real-life decision-making situations, experts tend to utilize linguistic information rather than numerical values to express their preferences or evaluations on alternatives. Considering the complexity of the decision-making problems, it is usually difficult to use single linguistic terms to elicit experts' preferences. Linguistic expressions that are close to cognition of human-being, such as comparative linguistic expression (CLE), are suggested to be applied in such cases. Three-way decision (3WD) has been proved an effective manner to handle multiattribute decision-making (MADM) problems, however there is a lack of 3WD methods dealing with linguistic expressions. By combining CLE and 3WD, a new multiattribute three-way group decision-making (3WGDM) method incorporating CLE (called CLE-3WGDM method) is proposed. A novel personalized numerical scale computation method, based on predecision in 3WGDM, is introduced to manage diverse interpretations of CLE for different individuals. Afterward, the attribute weights are calculated through a novel optimization model, which applies the principle of deviation maximization and minimum entropy of CLE. A novel 3WD-social network method is presented to compute the weights of experts. Comparative analysis with other existing methods have been carried out to verify the feasibility of the proposed one. Finally, the CLE-3WGDM method is applied to a traditional chinese medicine (TCM) decision problem.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".