A Novel Approach to Fuzzy Implication Through Fuzzy Linear Regression
Bibliographic record
Abstract
Fuzzy rule-based processes have traditionally incorporated a variety of fuzzy implications using modus ponens, modus tollens, and fuzzy negations.This study introduces a novel method of fuzzy implication utilizing Fuzzy Linear Regression (FLR) with triangular fuzzy numbers.This approach was applied to evaluate the relationship between parameters influencing concrete and the compressive strength of sustainable rice husk ash (RHA) concrete.FLR, a technique for modeling relationships between inputs and outputs in a fuzzy environment, was employed to determine a fuzzy output with a specific truth value.This truth value represented the degree of truth of an entire fuzzy implication.The data used in this study were derived from real experimental results.The analysis showed that the FLR method produced accurate outputs, as indicated by a low Theil's inequality coefficient (Theil's U=0.1).The results suggest that FLR can effectively manage uncertainties in data and holds potential as an alternative method for fuzzy implication.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".