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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".