ENHANCING EMAIL SPAM DETECTION THROUGH ENSEMBLE MACHINE LEARNING - A COMPREHENSIVE EVALUATION OF MODEL INTEGRATION AND PERFORMANCE

Aug 3, 2026

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Email spam detection and filtering function as essential security controls across organizations, helping to block unsolicited messages that constitute a large share of harmful traffic. The study applies machine learning classification using labeled features to distinguish spam from legitimate email. Because spam evolves rapidly and single-model approaches often misclassify, ensemble techniques with a meta-learning (stacking) design are introduced to aggregate multiple classifiers, reduce false positives and false negatives, and improve overall accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Abstract

Introduction and Related Works

Background on Email Spam Detection

Limitations of Single-Model Approaches

Ensemble Methods for Robust Detection

Bagging and Boosting

Random Forests

Manuscript 1451 ENHANCING EMAIL SPAM DETECTION THROUGH ENSEMBLE MACHINE LEARNING: A COMPREHENSIVE EVALUATION OF MODEL INTEGRATION AND PERFORMANCE Najah Al-shanableh Mazen S. Alzyoud Eman Nashnush Follow this and additional works at: [https://scholarworks.lib.csusb.edu/ciima](https://scholarworks.lib.csusb.edu/ciima) Part of the Management Information Systems Commons ENHANCING EMAIL SPAM DETECTION THROUGH ENSEMBLE MACHINE LEARNING: A COMPREHENSIVE EVALUATION OF MODEL INTEGRATION AND PERFORMANCE Dr. Najah Al-shanableh Computer Science Department, Al al-Bayt University, Mafraq, Jordan, [najah2746@aabu.edu.jo](najah2746@aabu.edu.jo) [Dr](Dr). Mazen Alzyoud Computer Science Department, Al al-Bayt University, Mafraq, Jordan, [malzyoud@aabu.edu.jo](malzyoud@aabu.edu.jo) [Dr](Dr). Eman Nashnushd School of Science, Engineering & Environment, University of Salford, Manchester, UK, [E.B.Nashnush@salford.ac.uk](E.B.Nashnush@salford.ac.uk) ABSTRACT Email spam detection and filtering are crucial security measures in all organizations. It is applied to filter unsolicited messages; most of the time, they comprise a large portion of harmful messages. Machine learning algorithms, specifically classification algorithms, are used to filter and detect if the email is spam or not spam. These algorithms entail training models on labelled data to predict whether an email is spam or not based on its features. In particular, traditional classification machine learning algorithms have been applied for decades but proved ineffective against fast-evolving spam emails. In this research, ensemble techniques by using the meta-learning approach are introduced to reduce the problem of misclassification of spam email and increase the performance of the combined model. This approach is based on combining different classification models to enhance the performance of detecting the spam emails by aggregating different algorithms to reduce false positives and false negative rates, and increase the accuracy of the combined model. The paper proposed ensemble techniques where various machine-learning algorithms are combined to improve the accuracy and strength of spam detection systems. Using different algorithms, it tries to create an appropriate systematic behaviour to increase the detection rates and reduce the number of misclassification cases. In this research, four machine learning algorithms were selected to build the meta-learning model; these algorithms have been chosen based on their proven effectiveness in spam detection systems, such as Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbours (KNN). The selected algorithms were applied individually on different datasets. Subsequently, an ensemble model was created using the stacking method to collect all the predictions of the models then aggregate and use them as input features for the final classifier that is based on the Logistic Regression algorithm. This study demonstrates the effectiveness of an ensemble approach for email spam detection by aggregating multiple weak machine learning algorithms to produce a strong machine learning model. The purpose of this research is to enhance the accuracy and robustness of the predictive model to detect spam emails. As a result, the proposed approach produced abetter performance with 95.8% accuracy. Keywords: Email Spam Detection, Ensemble Machine Learning algorithms, Meta-learning algorithms, Classification Algorithms. INTRODUCTION AND RELATED WORKS Email remains a vital communication tool in both personal and professional domains. However, the prevalence of spam emails poses significant challenges, leading to productivity loss and potential security threats. Traditional spam detection techniques, relying on single machine learning models, often fall short in handling the dynamic and sophisticated nature of spam. This paper explores an ensemble approach, integrating multiple machi

FAQs

Why is email spam detection important in organizations?

Email spam detection is crucial because spam messages are a major share of harmful communications, causing productivity loss and creating security risks.

What approach does the research use to improve spam classification?

The study introduces an ensemble strategy using meta-learning via stacking, combining several base classifiers and feeding their predictions into a final Logistic Regression classifier.

Which machine learning algorithms are selected for the ensemble?

The meta-learning model uses Naive Bayes, Support Vector Machine, Decision Tree, and K-Nearest Neighbours as individual base algorithms.

Aug 3, 2026

Upload by Maeve