A text feature selection method based on category-distribution divergence
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
The purpose of this paper is to overcome the problem that traditional feature selection methods [such as document frequency(DF), Chi-square statistic(CHI), information gain(IG), mutual information(MI) and Odds ratio(OR)] do not consider the distribution of features among different categories. The work aims at selecting the features that can accurately represent the theme of texts and to improve the accuracy of classification. In this paper, we propose a text feature selection method based on Category-Distribution Divergence, and the degree of membership and degree of non-membership are introduced into CDDFS (feature selection based on category-distribution divergence). CDDFS is used as a filter which can filter the features having low degree of membership and high degree of non-membership. CDDFS is tested with five feature selection methods and three classifiers using the corpus of Sogou Lab Data, and experimental results show that this method performs better than other feature selection methods when using KNN, and close to CHI when using Rocchio algorithms and SVM at high dimensions. This research proposes the representativeness and distinguishability of feature for category, and the representativeness and distinguishability of feature for non-category. If a feature has good distinguishability and high representativeness, then this feature will be retained in feature selection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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