A novel Data-driven fuzzy aggregation method for Takagi-Sugeno-Kang fuzzy Neural network system using ensemble learning
Bibliographic record
Abstract
Fuzzy aggregation operators commonly rely on expert information to solve multi-attribute decision-making problems. Such expert input may contain human bias or may sometimes be unavailable. This paper proposes a novel data-driven fuzzy aggregation method for Takagi-Sugeno-Kang fuzzy neural networks (TSKFNN) based upon the ensemble learning algorithm, AdaBoost. The objective of this research is to investigate whether ensemble learning is an effective tool for data-driven fuzzy aggregation. Our hypothesis is that ensemble learning would improve model performance and explainability. In this study, AdaBoost is applied to get a weighted combination of fuzzy rules in the TSKFNN and calculate the weighted average of these fuzzy rules to generate model predictions. Existing fuzzy aggregation operators are used as benchmarks to evaluate the proposed model. The results show that the proposed model is capable of yielding higher accuracy and greater interpretability than the existing methods through the identification of the most significant fuzzy rules used in the decision-making process.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".