A simple feature selection method for text classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In text classification most techniques use bag-of-words to represent documents. The main problem is to identify what words are best suited to classify the documents in such a way as to discriminate between them. Feature selection techniques are then needed to identify these words. The feature selection method presented in this paper is rather simple and computationally efficient. It combines a well known feature selection criterion, the information gain, and a new algorithm that selects and adds a feature to a bag-of-words if it does not occur too often with the features already in a small set composed of the best features selected so far for their high information gain. In brief, it tries to avoid considering features whose discrimination capability is sufficiently covered by already selected features, reducing in size the set of the features used to characterize the document set. This paper presents this feature selection method and its results, and how we have predetermined some of its parameters through experimentation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it