Optimization of Z-fuzzy soft $\beta$-covering based fuzzy rough sets and their application to multiple attribute group decision making
Bibliographic record
Abstract
Fuzzy sets play a crucial role in representing real-world complexities where precise binary logic falls short. Rough sets are closely related to fuzzy sets, and their combined use provides a powerful framework for handling uncertain and incomplete information.The combination of rough sets and fuzzy sets is an intriguing area of research that bridges the gap between crisp and uncertain information. In this paper, three different kinds of fuzzy serial relations are introduced. These relations form new fuzzy soft $\beta$ covering based fuzzy rough set models. The main objective of this research is to maximize the lower approximation and minimize the upper approximation of existing models. Eventually, we use the suggested rough set model to resolve MAGDM problems. The proposed MAGDM algorithm demonstrated superior performance when compared to other algorithms. Its effectiveness lies in its ability to find optimal solutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".