Bibliographic record
Abstract
Feature selection can decrease classifier size and improve accuracy by removing noisy and/or redundant features. However, it is possible for feature selection to yield features that are only partially informative about the classes in the set. These features are beneficial for distinguishing between some classes but not others. In these cases, it is beneficial to divide the large classification problem into a set of smaller problems, where a more specific set of features can be used to classify different classes. Dividing a problem this way is also common when the base classifier is binary, and the problem needs to be reformulated as a set of two-class problems so it can be handled by the classifier. This paper presents a method for multiclass classification that simultaneously formulates a binary tree of simpler classification subproblems and performs feature selection for the individual classifiers. The feature selected hierarchical classifier (FSHC) is tested against several well-known techniques for multiclass division. Tests are run on nine different real data sets and one artificial data set using a support vector machine (SVM) classifier. The results show that the accuracy obtained by the FSHC is comparable with other common multiclass SVM methods. Furthermore, the results demonstrate that the algorithm creates solutions with fewer classifiers, fewer features, and a shorter testing time than the other SVM multiclass extensions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".