A fuzzy structure similarity algorithm for attributed generalized trees
Bibliographic record
Abstract
In this paper, our vertex-attributed edge-attributed generalized tree structure proposed earlier is augmented using fuzzy attributes. Labels of vertices represent objects, while edge labels express fuzzy attributes. Edge weights represent the (percentage-)relative importance of fuzzy attributes, a kind of pragmatic information. The generalized trees are uniformly represented and interchanged using a fuzzy extension of Weighted Object Oriented RuleML. In the process of matching two generalized trees, a set of membership degrees related to the linguistic terms of fuzzy sets is assigned to each vertex using fuzzification of the numeric data of vertex labels. The fuzzy similarity of membership degrees related to each pair of corresponding vertex labels is computed, and the obtained fuzzy similarity value is considered in the structure similarity process. It is shown that this approach outperforms our earlier generalized tree similarity approach that considers exact string matching for computing the similarity of vertex labels. The use of our approach is demonstrated for life-insurance application underwriting.
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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 | 0.001 |
| 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".