On Some Feature Selection Strategies for Spam Filter Design
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
Feature selection is an important research problem in different statistical learning problems including text categorization applications such as spam email classification. In designing spam filters, we often represent the email by vector space model (VSM), i.e., every email is considered as a vector of word terms. Since there are many different terms in the email, and not all classifiers can handle such a high dimension, only the most powerful discriminatory terms should be used. Another reason is that some of these features may not be influential and might carry redundant information which may confuse the classifier. Thus, feature selection, and hence dimensionality reduction, is a crucial step to get the best out of the constructed features. There are many feature selection strategies that can be applied to produce the resulting feature set. In this paper, we investigate the use of hill climbing, simulated annealing, and threshold accepting optimization techniques as feature selection algorithms. We also compare the performance of the above three techniques with the linear discriminate analysis. Our experiment results show that all these techniques can be used not only to reduce the dimensions of the e-mail, but also improve the performance of the classification filter. Among all the strategies, simulated annealing has the best performance which reaches a classification accuracy of 95.5%
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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 | 0.001 |
| 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 it