Dombi aggregation operator in terms of complex bipolar fuzzy sets with application in decision making problems
Bibliographic record
Abstract
The complex bipolar fuzzy sets fundamentally expand upon bipolar fuzzy sets and complex fuzzy set, and demonstrate efficacy in dealing with two-dimensional uncertainty, specifically where amplitude and phase information are both relevant. However, existing aggregation operators within the complex bipolar fuzzy environment generally fail to provide ample flexibility and generalization to accommodate broader range of decision-making scenarios. To overcome this deficiency, this research puts forward four novel aggregation operators based on Dombi operations: Complex bipolar Dombi fuzzy weighted arithmetic aggregation operator, Complex bipolar Dombi fuzzy weighted geometric aggregation operator, Complex bipolar Dombi fuzzy ordered weighted arithmetic aggregation operator, and Complex bipolar Dombi fuzzy ordered weighted geometric aggregation operator. These operators incorporate operational parameters to enhance adaptability and accuracy in aggregation processes. In this research, we propose the mathematical formulation and fundamental properties of these operators within the complex bipolar fuzzy framework. To demonstrate the usefulness of the proposed method, a case study involving the selection of a buffalo with the objective of profit maximization and the best possible return on investment is presented. The outcomes confirm that the suggested operators offer enhanced flexibility and efficiency over existing aggregation methods. The comparative study additionally demonstrates their distinct advantages, making them valuable tools for complex bipolar fuzzy decision-making applications.
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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 | 0.003 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".