Design and development of dynamic collaborative frameworks using concepts of knowledge-based networks
Bibliographic record
Abstract
We have developed a suite of dynamic frameworks for clustering, classification, and regression problems of knowledge-based networks using collaborative approaches. For solving clustering problems, we provide a formulation of the model-integration problem using the principles of sharing prototypes and membership-functions and describe iterative algorithms that converge to an optimal solution. We show that the measure of proximity-distance is a suitable vehicle for quantifying the consensus of collaborative data sites. For classification and regression problems, we present a new experience-consistent framework. By extending the performance index, we show that the domain knowledge captured by regression and classification models plays a regularization role in system identification problems. We demonstrate that the achieved consistency between collaborative sites can be quantified through fuzzy sets related to the parameters of the model. In the development of an approach to fuzzy rule-based model identification realized in a collaborative framework of experiential evidence (data) and knowledge evidence (past experience), we demonstrate how to reconcile these two essential sources of guidance in the form of local regression models. Using a radial-basis function neural networks approach, consistency is achieved using a connection value framework to reconcile data with past experience by considering gradient-based neural networks method. The study provides architectural considerations, elaborates on essential communication mechanisms, and covers underlying algorithmic aspects of knowledge-based networks. We explain how the collaboration mechanism gives rise to higher order granular constructs such as type-2 fuzzy sets that emerge in a highly legitimate manner in distributed fuzzy modeling. We evaluate our methods with type-2 fuzzy sets. The theoretical and algorithmic approaches to collaborative frameworks investigated in this study can be used as a foundation for further research in the area of distributed fuzzy modeling.
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 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".