Naive Bayes text classifiers: a locally weighted learning approach
Why this work is in the frame
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Bibliographic record
Abstract
Due to being fast, easy to implement and relatively effective, some state-of-the-art naive Bayes text classifiers with the strong assumption of conditional independence among attributes, such as multinomial naive Bayes, complement naive Bayes and the one-versus-all-but-one model, have received a great deal of attention from researchers in the domain of text classification. In this article, we revisit these naive Bayes text classifiers and empirically compare their classification performance on a large number of widely used text classification benchmark datasets. Then, we propose a locally weighted learning approach to these naive Bayes text classifiers. We call our new approach locally weighted naive Bayes text classifiers (LWNBTC). LWNBTC weakens the attribute conditional independence assumption made by these naive Bayes text classifiers by applying the locally weighted learning approach. The experimental results show that our locally weighted versions significantly outperform these state-of-the-art naive Bayes text classifiers in terms of classification accuracy.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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