Bibliographic record
Abstract
Bayesian belief nets (BNs) are often used for classification tasks, typically to return the most likely class label for a specified instance. Many BN-learners, however, attempt to find the BN that maximizes a different objective function — viz., likelihood, rather than classification accuracy — typically by first using some model selection criterion to identify an appropriate graphical structure, then finding good parameters for that structure. This paper considers a number of possible criteria for selecting the best structure, both generative (i.e., based on likelihood; BIC, BDe) and discriminative (i.e., Conditional BIC (CBIC), resubstitution Classification Error (CE) and Bias 2 +Variance (BV)). We empirically compare these criteria against a variety of different “correct BN structures”, both real-world and synthetic, over a range of complexities. We also explore different ways to set the parameters, dealing with two issues: (1) Should we seek the parameters that maximize likelihood versus the ones that maximize conditional likelihood? (2) Should we use (i) the entire training sample first to learn the best parameters and then to evaluate the models, versus (ii) only a partition for parameter estimation and another partition for evaluation (cross-validation)? Our results show that the discriminative BV model selection criterion is one of the best measures for identifying the optimal structure, while the discriminative CBIC performs poorly; that we should use the parameters that maximize likelihood; and that it is typically better to use cross-validation here.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".