A Majority Rule-Based Measure for Atanassov-Type Intuitionistic Membership Grades in MCDM
Bibliographic record
Abstract
Orderly Atanassov-type intuitionistic membership grades would be required in decision-making problems, however, sometime they are not completely ordered. To solve this problem, in this article we propose a quantification method for Atanassov-type intuitionistic membership grades, and use it to rank them. According to the majority voting rules, we introduce the measurement function for membership degree. We quantify the uncertainty of information and the preferences of decision-makers conveyed through intuitionistic fuzzy sets. We then use the introduced surrogates to construct the measurement for membership grades. The properties and some logical operations of measurement value are also studied. We recommend using the Takagi–Sugeno model and method to assign values to tuning parameters$K$. Moreover, we present two models for multicriteria decision-making problem, which use the measurement to determine the ranking between sets. Finally, a numerical example of supplier selection is given to show the competitive performance of the proposed method in terms of efficiency and feasibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".