Personalized Spam Filtering with Natural Language Attributes
Bibliographic record
Abstract
Email spam is one of the biggest threats to today's Internet. To deal with this threat, many anti-spam filters have been developed. One big challenge for these filters is to predict the labels of emails in a personalized mailbox. In this paper, we report the performance of an anti-spam filter named Sentinel. In addition to some commonplace attributes, Sentinel uses attributes related to natural language stylometry. The filter has been tested with six benchmark datasets in the Enron-Spam collection. Classifiers generated by well-known meta-learning algorithms like AdaBoostM1 and Bagging perform equally the best, while a Random Forest (RF) generated classifier performs almost as well. The performance of classifiers using Support Vector Machine (SVM) and Naive Bayes (NB) are not satisfactory. Comparisons show that the performance of Sentinel surpasses that of a number of state-of-the-art personalized filters proposed in previous studies.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 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".