On Improving the Performance of Spam Filters Using Heuristic Feature Selection Techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Electronic mail (E-mail) has become extremely important in our daily life because of its high speed and low cost. Unfortunately, every day, E-mail users receive increasing number of unwanted spam E-mails from different sources. Spam E-mail has become an annoying, costly and time-consuming problem for many people. In fact, spam E-mail has become one of the most common ISP customer service complaints and one of the main reasons behind most subscriber churn. One popular means for solving the spam problem is to deploy an email filter to classify the spam and legitimate E-mails. However, the accuracy of most of the current solutions still needs further improvement. In this paper, we present two heuristic feature selection algorithms that can be used to improve the accuracy of spam email filters. In particular, we experiment the application of both the artificial immune systems (AIS) and tabu search (TS) as classifier dependent feature selection techniques for email filter. We also compare the performance of our proposed solution with the classical singular value decomposition (SVD) based system. Using a K-nearest neighbor (KNN) classifier, the accuracy of the AIS and the tabu search based systems is 90.9% and 94.5% respectively as compared to 90% for the SVD based system
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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 | 0.000 | 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 | 0.000 |
| 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 it