E-mail Classification Using Horse Herd Optimization Algorithm

Key words:  E-mail classification  optimization technique  support vector machine  binary classification  machine learning

Abstract:

In recent decades, the proliferation of email communication has markedly escalated, resulting in a concomitant surge in spam emails that congest networks and present security risks. This study introduces an innovative spam detection method utilizing the Horse Herd Optimization Algorithm (HHOA), designed for binary classification inside a multi-objective framework. The method proficiently identifies essential features, minimizing redundancy and improving classification precision. The suggested HHOA attained an impressive accuracy of 97.21% on the Kaggle email dataset, with a precision of 94.30%, a recall of 90.50%, and an F1-score of 92.80%. In comparison to conventional techniques, such as Support Vector Machine (93.89% accuracy), Random Forest (96.14% accuracy), and K-Nearest Neighbours (92.08% accuracy), HHOA exhibited enhanced performance with diminished computing complexity. The suggested method demonstrated enhanced feature selection efficiency, decreasing the number of selected features while preserving good classification accuracy. The results underscore the efficacy of HHOA in spam identification and indicate its potential for further applications in practical email filtering systems.