Bibliographic record
Abstract
Considering the inherent granularity present in fuzzy modeling, the objective of this chapter is to endow fuzzy models with an important feature of evolvable granularity—granularity whose level mirrors the varying dynamics and perception of the data/system. Depending on the architecture of the fuzzy models, the changes in granularity can be directly translated into the number of rules, local models, number of fuzzy neurons, and so on. The authors revisit and redevelop Fuzzy C-Means (FCM) so that the generic algorithm could be efficiently used in the framework of dynamic data analysis. The chapter concentrates on the fundamental design aspects, namely (1) splitting and merging criteria, (2) assessment of quality of clusters that could be directly used when controlling the dynamics of the clusters and deciding on the change of the number of clusters themselves, and (3) optimization capabilities of fuzzy clustering (FCM), which could be exploited upfront when running the algorithm. Controlled Vocabulary Terms fuzzy neural nets; merging; optimisation; workstation clusters
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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) | 0.000 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".