Anti-spam Filter Based on Data Mining and Statistical Test.
Bibliographic record
Abstract
Because of the popularity of Internet and wide use of E-mail the volume of spam mails keeps growing rapidly. The growing volume of spam mails annoys people and affects work efficiency significantly. Most previous researches focused on developing spam filtering algorithm, using statistics or data mining approach to develop precise spam rules. However, mail servers may generate new spam rules constantly and mail server will then carry a growing number of spam rules. The rules might be out-of-date or imprecise to classification as spam evolves continuously and hence applying such rules might cause misclassification. In addition, too many rules in mail server may affect the performance of mail filters. In this research, we propose an anti-spam approach combining both data mining and statistical test approach. We adopt data mining to generate spam rules and statistical test to evaluate the efficiency of them. By the efficiency of spam rules, only significant rules will be used to classify emails and the rest of rules can be eliminated then for performance improvement.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".