A boosting algorithm for learning bipartite ranking functions with partially labeled data
Bibliographic record
Abstract
This paper presents a boosting based algorithm for learning a bipartite ranking function (BRF) with partially labeled data. Until now different attempts had been made to build a BRF in a transductive setting, in which the test points are given to the methods in advance as unlabeled data. The proposed approach is a semi-supervised inductive ranking algorithm which, as opposed to transductive algorithms, is able to infer an ordering on new examples that were not used for its training. We evaluate our approach using the TREC-9 Ohsumed and the Reuters-21578 data collections, comparing against two semi-supervised classification algorithms for ROCArea (AUC), uninterpolated average precision (AUP), mean [email protected] (TP) and Precision-Recall (PR) curves. In the most interesting cases where there are an unbalanced number of irrelevant examples over relevant ones, we show our method to produce statistically significant improvements with respect to these ranking measures.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".