A Noise Resilient and Non-parametric Graph-based Classifier
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Bibliographic record
Abstract
In this paper, we propose a non-parametric and noise resilient graph-based classification algorithm. In designing the proposed method, we represent each class of dataset as a set of sub-graphs. The main part of the training phase is how to build the classification graph based on the non-parametric k-associated optimal graph algorithm which is an extension of the parametric k-associated graph algorithm. In this paper, we propose a new extension and modification of the training phase of the k-associated optimal graph algorithm. We compare the modified version of the k-associated optimal graph (MKAOG) algorithm with the original k-associated optimal graph algorithm (KAOG). The experimental results demonstrate superior performance of our proposed method in the presence of different levels of noise on various datasets from the UCI repository.
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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 | 0.001 | 0.001 |
| 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 it