Pattern Discovery and Disentanglement: A New Interpretable Machine Learning Paradigm for Relational Datasets
Bibliographic record
Abstract
Abstract In machine learning (ML) on relational datasets, association patterns in the data, paths in decision trees, and weights between layers of the neural network coming from multiple underlying sources are often entangled, masking the pattern-to-source relation, weakening prediction and defying explanation. This paper presents a revolutionary ML paradigm: Pattern Discovery and Disentanglement (PDD), which disentangles associations and provides an All-in-One knowledge framework and computational platform, capable of a) disentangling patterns to associate with distinct sources/classes; b) discovering rare/imbalanced groups, detecting anomalies and rectifying discrepancies to improve class association, pattern/entity clustering; c) organizing knowledge for interpretability/traceability for causal exploration with statistical support. Results from base studies validate such capabilities. The explainable knowledge reveals pattern-source relations, entity characteristics and underlying factors for causal inference, clinical study, and practice, addressing the major concern of interpretability, trust and reliability when applying ML to healthcare --- a step towards closing the AI chasm.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".