LNBC: A Link-Based Naive Bayes Classifier
Bibliographic record
Abstract
Many databases store data in relational format, with different types of entities and information about links between the entities. Link-based classification is the problem of predicting the class label of a target entity given information about features of the entity and about features of the related entities. A natural approach to link-based classification is to upgrade standard classification methods from the propositional, single-table testing. In this paper we propose a new classification rule for upgrading naive Bayes classifiers (NBC). Previous work on relational NBC has achieved the best results with link independency assumption which says that the probability of each link to an object is independent from the other links to the object. We formalize our method by breaking it into two parts: (1) the independent influence assumption: that the influence of one path from the target object to a related entity is independent of another. We consider object-path independency and (2) the independent feature assumption of NBC: that features of the target entity and a related entity are probabilistically independent given a target class label. We derive a new relational NBC rule that places more weight on the target entity features than formulations of the link independency assumption. The new NBC rule yields higher accuracies on three benchmark datasets-Mutagenesis, MovieLens, and Cora-with average improvements ranging from 2% to 10%.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".