A Method of Finding Representative Sets of Rules
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
The use of rough sets theory to select essential attributes that can represent the original data set is well known. Knowledge discovered from such essential attributes are typically represented as rules, and are therefore represen- tative of the original data. We present three results towards rule evaluation as an extension of the "Rules-as-Attributes measure". First, we present an approach of finding repre- sentative sets of rules for a given data set. Secondly, we suggest that the Johnson's Reducer of the ROSETTA soft- ware generates a reduct with the minimum number of rules, and can be considered as a minimum representation of the original knowledge. Our third result provides an integrated approach for rule evaluation based on both the Rule Impor- tance Measure and the method of finding representative sets of rules. We argue that this approach can take the represen- tative rules ranking into a further stage. These approaches are proposed to facilitate the rule evaluations and can pro- vide an automatic and complete comprehension of the orig- inal data set.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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