Design of Distributed Rule-Based Models in the Presence of Large Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Generally, fuzzy models, especially rule-based models, are designed in a monolithic manner, meaning that all data are used <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">en bloc</i> to design the model. At the same time, there is a visible need to cope with the ever-increasing volumes of data (both in terms of the number of data and their dimensionality) as well as being faced with distributed data located at various locations. The objective of this article is to develop a concept and provide a design framework as well as assess its performance for constructing a collection of rule-based models on a basis of a randomly sampled repository of data and then realize their aggregation. More specifically, for the sampled data, the design of each model is carried out in a standard way as commonly encountered in the case of Takagi–Sugeno (TS) rule-based models and next augmented by gradient boosting. The aggregation is realized by optimizing a weighting scheme applied to the results of the individual models. Our intent is also to carefully demonstrate the performance offered by the mechanisms of machine learning applied in the setting of rule-based models, which is an original task completed before. A number of high-dimensional data are used in the experimental studies to complete a thorough assessment. A comparative performance analysis is reported with respect to the monolithically developed TS models.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it