Proactive MiDLAF: A Novel Mining MinHash-Deep Learning Approach for Advanced Spam Email Filtering
Bibliographic record
Abstract
Spam email filtering has recently become the most important task helping in maintaining secure and efficient communication systems.As spam emails lead to security leach, reduced productivity, and increased storage costs, this paper is intended to present a proactive approach to spam email classification, leveraging the advanced techniques to increase detection accuracy and efficiency.The proposed work consists of the three steps.The preprocessing step introduces MinHash which provides a small signature matrix for fast approximation based on a k-shingle technique that generates overlapping sequences of k word, effectively capturing the context and nuances of the spam email text.The second step uses the advanced techniques of machine learning (ML) Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Multi-Layer Perceptron (MLP), Logistic Regression, and K-Nearest Neighbors (KNN), and Long Short-Term Memory (LSTM) for deep learning (DL) to classify ham and spam emails.The outcomes illustrate that combining the k-shingle, MinHash with advanced text for feeding ML and DL results in high accuracy rate compared with the other works where the SVM classifiers achieves accuracy rate of 98.95% highlighting its effectiveness in distinguishing between ham and spam emails.Other ML shows competitive performance, With MLP 98.25%, RF 95.6%, Logistic regression 98%, DT 93.3%, and lowest accuracy with KNN 70.1%.DL satisfies a high accuracy rate up to 96.1%.This work contributes to the development of a scalable and reliable solution for spam filtering in modern communication systems.
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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".