Linear programming and quadratic programming approach for graduation in fuzzy environment
Bibliographic record
Abstract
Decision making under uncertainty has become a key issue in the present alternative way of thinking. There is an emerging interest in the use of new techniques to draw definite conclusions from imprecise or vague information in order to take competitive advantage. A critical challenge in decision-making process is not only to find a suitable method to measure and quantify the uncertainty involved in the problem under consideration but also its successful applications. In the present thesis, we consider a graduation problem with imprecise observed values data and imprecise combination of fit and smoothness. The problem is first formulated, solved and analyzed as a fuzzy linear program. Next, a finite iteration technique is developed to solve a fuzzy quadratic programming problem. Significance of this model can be hopefully seen in the light of usage of quadratic program in the field of Finance, Economics, Structural Engineering and Actuarial Sci nces under uncertainty. Furthermore, the graduation problem is revisited using fuzzy quadratic programming model and solutions are obtained both under crisp and fuzzy environment. The results so obtained are shown to be better than the results obtained by using fuzzy linear programming, and the results obtained by Schuette using crisp linear programming. The methods introduced in the present thesis, offer an opportunity to view a graduation problem from a different prospective.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".