Comparative Analysis of Classification Algorithms for Email Spam Detection

Abstract

Abstract-The increase in the use of email in every day transactions for a lot of businesses or general communication due to its cost effectiveness and efficiency has made emails vulnerable to attacks including spamming. Spam emails also called junk emails are unsolicited messages that are almost identical and sent to multiple recipients randomly. In this study, a performance analysis is done on some classification algorithms including: Bayesian Logistic Regression, Hidden Naï ve Bayes, Radial Basis Function (RBF) Network, Voted Perceptron, Lazy Bayesian Rule, Logit Boost, Rotation Forest, NNge, Logistic Model Tree, REP Tree, Naï ve Bayes, Multilayer Perceptron, Random Tree and J48. The performance of the algorithms were measured in terms of Accuracy, Precision, Recall, FMeasure, Root Mean Squared Error, Receiver Operator Characteristics Area and Root Relative Squared Error using WEKA data mining tool. To have a balanced view on the classification algorithms' performance, no feature selection or performance boosting method was employed. The research showed that a number of classification algorithms exist that if properly explored through feature selection means will yield more accurate results for email classification. Rotation Forest is found to be the classifier that gives the best accuracy of 94.2%. Though none of the algorithms did not achieve 100% accuracy in sorting spam emails, Rotation Forest has shown a near degree to achieving most accurate result.

Machine Learning Based Email Spam Detection: Achieving High Accuracy and Efficiency

“…The paper [9] explores SMS spam detection using a bagging approach with RVM, SVM, Naive Bayes, and KNN algorithms, highlighting the effectiveness of the RVM model, which achieved the best performance with an F1 score of 0.975175, and emphasizing the importance of accurate SMS spam classification. The literature review of the paper [10] focuses on the increase in email usage for business transactions and communication, highlighting the vulnerability of emails to spam attacks. It discusses the importance of data mining in addressing the spam issue and the use of classification algorithms for spam detection.…”

Three Approaches to Client Email Topic Classification

“…Researchers still experiment with different techniques and approaches in order to improve the existing SPAM and Phishing email classifiers. Iqbal and Khan (2022) achieved the 98.06% accuracy using the binary Support Vector Machine (SVM) classifier for the case of SPAM, whilst Shuaib et al (2018) developed the optimal SPAM classifier using Rotation Forest algorithm, achieving the accuracy of 94.2%. Ali et al (2021) experimented with feature engineering and RNN/CNN architectures, concluding that RNN provides the highest 94.9% accuracy.…”

Performance Evaluation of Email Spam Text Classification Using Deep Neural Networks

“…Drucker et al [7] proposed SVM algorithm on sample emails dataset that have attained 90-95% accuracy. Abdulhamid et al [8] proposed a machine learning algorithm on UCI Machine learning repository dataset and achieved 94.2% accuracy. DeBarr and Wechsler [9] proposed a Random forest algorithm on custom collection dataset.…”