Bibliographic record
Abstract
Employing the most relevant and discriminating features is very important to achieve a successful classification with low computational cost. Although, different feature selection methods have been recently developed for this purpose, feature grouping can deal with high dimensional sparse feature vectors more effectively, yielding better interpretation of the data. In this paper, a correlation-based feature grouping (CFG) method is proposed. First, the features are grouped based on the variety of their correlation scores, and then, a new representative feature vector is generated for each group by combining its features. To investigate the strength of CFG method, two filter methods of χ2and correlation are employed for feature selection, while classification is performed using a support vector machine (SVM) and k-Nearest Neighbor (k-NN). The empirical study on two datasets of protein-protein interactions (PPIs) and breast cancer verifies that the idea of employing feature grouping is more efficient than employing feature selection in identifying a set of features that exhibit high classification accuracy. In addition, a CFG diagram is introduced in this paper, which is used to visualize the groups and their corresponding features found by the proposed method.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".