Dynamic Recursive Fuzzy ART Multi-label Classifier
Why this work is in the frame
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Bibliographic record
Abstract
A new approach based on the Fuzzy Adaptive Resonance Theory (ART) network, called the Dynamic Recursive Fuzzy ART Classifier (DyRFAC), is presented for incremental supervised learning on multi-label datasets. Whereas many ART-based algorithms rely on a single, unvarying vigilance parameter, our classifier employs a dynamic vigilance mechanism, enabling finer-grained partitioning of the data space. Through recursive splitting guided by a purity measure, DyRFAC iteratively adjusts category boundaries, allowing the category space to more accurately capture the data distribution. In addition, DyRFAC introduces a category merging procedure to control the growth of categories when data scales, preventing excessive proliferation that could degrade model performance. We conduct experiments comparing DyRFAC with other multi-label classification algorithms, demonstrating its competitive performance on complex label distributions.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it