Aggregating predictions vs. aggregating features for relational classification
Bibliographic record
Abstract
Relational data classification is the problem of predicting a class label of a target entity given information about features of the entity, of the related entities, or neighbors, and of the links. This paper compares two fundamental approaches to relational classification: aggregating the features of entities related to a target instance, or aggregating the probabilistic predictions based on the features of each entity related to the target instance. Our experiments compare different relational classifiers on sports, financial, and movie data. We examine the strengths and weaknesses of both score and feature aggregation, both conceptually and empirically. The performance of a single aggregate operator (e.g., average) can vary widely across datasets, for both feature and score aggregation. Aggregate features can be adapted to a dataset by learning with a set of aggregate features. Used adaptively, aggregate features outperformed learning with a single fixed score aggregation operator. Since score aggregation is usually applied with a single fixed operator, this finding raises the challenge of adapting score aggregation to specific datasets.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".