A disaggregation approach for indirect preference elicitation in Electre <scp>TRI‐nC</scp>: Application and validation
Bibliographic record
Abstract
Abstract Multicriteria sorting methods are often used in decision aiding contexts where the objective is to assign alternatives to predefined ordered categories. The Electre Tri family of sorting methods is based on pairwise comparisons of the alternatives with some, possibly fictional, alternatives that are either upper or lower limits of the categories (Electre Tri‐B), or one or more typical reference alternatives, that is, representative categories profiles (Electre Tri‐C, Tri‐nC). In this paper, we are interested in the Electre Tri‐nC method and in indirect preference elicitation based on partial information provided by the Decision Maker. We therefore propose, apply and evaluate a preference disaggregation method for learning criteria weights and the credibility threshold used in Electre Tri‐nC. The proposed disaggregation method is validated in an experiment using a climate classification problem for light tourism where 62,482 touristic locations are sorted into four categories. A robustness analysis of the method's performance using 150 learning sets is conducted and the results are presented and discussed.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".