Analysis and comparison of machine learning methods and improved SVM algorithm in spam classification
Bibliographic record
Abstract
The most popular form of official communication for business purposes is email. Despite the existence of other communication methods, email usage is still the largest. Today's environment necessitates automated email management due to the daily increase in email volume. More than 55% of emails users received nowadays are flagged as spam. This exemplifies how these spams squander the time and resources of email users while creating nothing beneficial. Understanding the various spam email categorization strategies and how they operate is essential since spammers employ complex and creative techniques to carry out their illicit operations through spam emails. The comparison to find the most accuracy machine learning-based spam categorization methods such Naïve Bayes, SVM, and random forest is the initial objective of this work, after that the paper compares the initial result with the improved SVM algorithm. This study provides a comprehensive analysis and assessment of earlier studies on various machine learning methods, email properties, and methodologies. The results show that the improved support vector machine obtains a good email classification effect and can meet the requirements of spam processing Introduction
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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 | 0.002 |
| 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 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".