Partitioning trees: A global multiclass classification technique for SVMs
Bibliographic record
Abstract
Presented in this paper is a novel technique for multiclass classification in SVMs through combination of binary classifiers,namely that of Partitioning Trees (P-Trees). The technique aims at improving the Directed Acyclic Graphs (DAGs) both interms of training as well as testing performance. It works by progressively constructing a decision graph, where each node is abinary classifier. Each trained node defines a dichotomy over the instance space which, in turn, is used to train subsequent nodes.In this way, every node trains against only a subset of the samples of its classes; namely the samples that reach the node throughthe decision graph in addition to a subsampled version of the ones that fail to reach it. Training sets reduce in size and decisionsurfaces become more compact, thus improving training and testing performance. Extensive experimental results demonstratethe effectiveness of the proposed technique in reducing the training and testing time in SVMs, while maintaining comparablegeneralization performance to the 1vs1 and DAGs techniques.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".