Improve Decision Tree for Probability-Based Ranking by Lazy Learners
Bibliographic record
Abstract
Existing work shows that classic decision trees have inherent deficiencies in obtaining a good probability-based ranking (e.g. AUC). This paper aims to improve the ranking performance under decision-tree paradigms by presenting two new models. The intuition behind our work is that probability-based ranking is a relative metric among samples, therefore, distinct probabilities are crucial for accurate ranking. The first model, Lazy Distance-based Tree (LDTree), uses a lazy learner at each leaf to explicitly distinguish the different contributions of leaf samples when estimating the probabilities for an unlabeled sample. The second model, Eager Distance-based Tree (EDTree), improves LDTree by changing it into an eager algorithm. In both models, each unlabeled sample is assigned a set of unique probabilities of class membership instead of a set of uniformed ones, which gives finer resolution to differentiate samples and leads to the improvement of ranking. On 34 UCI sample sets, experiments verify that our models greatly outperform C4.5, C4.4 and other standard smoothing methods designed for better ranking.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".