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Main Author: Majed Molhi
Format: Recurso digital
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Published: Zenodo 2026
Online Access:https://doi.org/10.5281/zenodo.19698281
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author Majed Molhi
author_facet Majed Molhi
contents <h2>First Release</h2> <p>This release includes the complete implementation of SMS spam detection using PSO-based feature selection with Naive Bayes and Logistic Regression classifiers.</p> <h3>Results</h3> <ul> <li>NB After PSO: Accuracy 97.04% | F1 89.04% | Features: 723 (51.8% reduction)</li> <li>LR After PSO: Accuracy 97.49% | F1 90.73% | Features: 762 (49.2% reduction)</li> </ul> <h3>Contents</h3> <ul> <li>Full implementation notebook</li> <li>Dataset: SMS Spam Collection (UCI)</li> </ul>
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_19698281
institution Zenodo
language
publishDate 2026
publisher Zenodo
record_format zenodo
spellingShingle majedmolhi/SMS-Spam-Detection-PSO: SMS Spam Detection Using PSO-Based Feature Selection v1.0.0
Majed Molhi
<h2>First Release</h2> <p>This release includes the complete implementation of SMS spam detection using PSO-based feature selection with Naive Bayes and Logistic Regression classifiers.</p> <h3>Results</h3> <ul> <li>NB After PSO: Accuracy 97.04% | F1 89.04% | Features: 723 (51.8% reduction)</li> <li>LR After PSO: Accuracy 97.49% | F1 90.73% | Features: 762 (49.2% reduction)</li> </ul> <h3>Contents</h3> <ul> <li>Full implementation notebook</li> <li>Dataset: SMS Spam Collection (UCI)</li> </ul>
title majedmolhi/SMS-Spam-Detection-PSO: SMS Spam Detection Using PSO-Based Feature Selection v1.0.0
url https://doi.org/10.5281/zenodo.19698281