A new fuzzy inference approach based on mamdani inference using discrete type 2 fuzzy sets
Bibliographic record
Abstract
Fuzzy system modeling (FSM) is one of the most prominent system modeling tools in analyzing the data in the presence of uncertainty. Linguistic fuzzy rulebase (LFR) structure, in which both the antecedent and consequent variables are represented by fuzzy sets, is the most well known fuzzy rulebase structure in the literature. The proposed FSM method identifies LFR system model by executing fuzzy C-Means (FCM) clustering method. One of the sources of uncertainty in system modeling is the uncertainty in selecting learning parameters. In order to capture this uncertainty in a more realistic way, the antecedent and consequent variables are represented by using type 2 fuzzy sets that are constructed by executing FCM method with different level of fuzziness, m, values. The proposed system modeling approach is applied on a well-known benchmark data set where the goal is to predict the price of a stock. After comparing the results with the ones obtained with other system modeling tools, it can be claimed successful results are achieved.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".