A text feature selection method based on category-distribution divergence
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.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.001 | 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 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".