Main Article Content
Email is one of the most popular communication tools for most internet users
nowadays. It has become fast and an effective method to share and exchange information all
over the world. Despite the great advantages of emails, its usage is facing problem which is spam
emails. Spam emails are the huge presence of bulk and unsolicited emails which are expensive for
the companies, consume a huge amount of mail servers, network bandwidth and waste of time.
Isolating and detecting these emails is known as spam detection. Many spam detection methods
have been proposed but there is still need to detect the email spam effectively with high accuracy.
In this paper, hybrid particle swarm optimization and Pegasos algorithm, which is called (PSOPegasos) is proposed for spam email detection. Particle swarm optimization is employed as a
search strategy to determine the optimal parameters for Pegasos algorithm in order to achieve
higher performance. The proposed algorithm has been applied on spambase dataset downloaded
from UCI Machine Learning Repository. Experimental results demonstrate that the proposed
algorithm outperforms the performance of all the earlier proposed algorithms, considering the
accuracy, recall, precision and F-measure on the same dataset.