Bibliographic record
Abstract
We present a novel investigation of email clustering, demonstrating that clustering can be a powerful tool for email spam filtering. We first extend the well-known notion that ham and spam emails can be divided into clusters, showing the striking result that almost any reasonable clustering algorithm will naturally partition an email dataset into almost entirely spam and entirely spam clusters. We then consider the specific semi-supervised spam filtering scenario of filtering when a large amount of training data is available, but only a few true labels can be obtained for that data. We present two spam filtering approaches for this scenario, both of which start with a clustering of training email. Our first approach uses the true labels of the medoids of each cluster to train a spam filter; our second approach functions similar to the first, except that the true label of each cluster's medoid is used as the label of every email within the cluster, giving a much larger set of labels for training, while still only requiring only a few labels. We evaluate our approaches using the TREC2005 and CEAS2008 spam email datasets. For a large range of different numbers of true labels, we show that both of our approaches significantly outperform training on the same number of randomly selected email messages. The results of our second approach are also better than those of a previously published state-of-the-art semi-supervised small sample spam filtering approach.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".