Categorical proportional difference: a feature selection method for text categorization
Bibliographic record
Abstract
Supervised text categorization is a machine learning task where a predefined category label is automatically assigned to a previously unlabelled document based upon characteristics of the words contained in the document. Since the number of unique words in a learning task (i.e., the number of features) can be very large, the efficiency and accuracy of the learning task can be increased by using feature selection methods to extract from a document a subset of the features that are considered most relevant. In this paper, we introduce a new feature selection method called categorical proportional difference (CPD), a measure of the degree to which a word contributes to differentiating a particular category from other categories. The CPD for a word in a particular category in a text corpus is a ratio that considers the number of documents of a category in which the word occurs and the number of documents from other categories in which the word also occurs. We conducted a series of experiments to evaluate CPD when used in conjunction with SVM and Naive Bayes text classifiers on the OHSUMED, 20 Newsgroups, and Reuters-21578 text corpora. Recall, precision, and the F-measure were used as the measures of performance. The results obtained using CPD were compared to those obtained using six common feature selection methods found in the literature: χ 2, information gain, document frequency, mutual information, odds ratio, and simplified χ 2. Empirical results showed that, in general, according to the F-measure, CPD outperforms the other feature selection methods in four out of six text categorization tasks.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".