Open Access
ARTICLE
Email Filtering Using Hybrid Feature Selection Model
Adel Hamdan Mohammad1,* , Sami Smadi2, Tariq Alwada’n3
1
Computer Science Department, The World Islamic Sciences and Education University, Amman, Jordan
2
Information System and Networks Department, The World Islamic Sciences and Education University, Amman, Jordan
3
Network and Cybersecurity Department, Teesside University, Middlesbrough, UK
* Corresponding Author: Adel Hamdan Mohammad. Email:
Computer Modeling in Engineering & Sciences 2022, 132(2), 435-450. https://doi.org/10.32604/cmes.2022.020088
Received 03 November 2021; Accepted 21 January 2022; Issue published 15 June 2022
Abstract
Undoubtedly, spam is a serious problem, and the number of spam emails is increased rapidly. Besides, the massive
number of spam emails prompts the need for spam detection techniques. Several methods and algorithms are used
for spam filtering. Also, some emergent spam detection techniques use machine learning methods and feature
extraction. Some methods and algorithms have been introduced for spam detecting and filtering. This research
proposes two models for spam detection and feature selection. The first model is evaluated with the email spam
classification dataset, which is based on reducing the number of keywords to its minimum. The results of this model
are promising and highly acceptable. The second proposed model is based on creating features for spam detection
as a first stage. Then, the number of features is reduced using three well-known metaheuristic algorithms at the
second stage. The algorithms used in the second model are Artificial Bee Colony (ABC), Ant Colony Optimization
(ACO), and Particle Swarm Optimization (PSO), and these three algorithms are adapted to fit the proposed model.
Also, the authors give it the names AABC, AACO, and APSO, respectively. The dataset used for the evaluation of
this model is Enron. Finally, well-known criteria are used for the evaluation purposes of this model, such as true
positive, false positive, false negative, precision, recall, and F-Measure. The outcomes of the second proposed model
are highly significant compared to the first one.
Keywords
Cite This Article
Mohammad, A. H., Alwada’n, T. (2022). Email Filtering Using Hybrid Feature Selection Model.
CMES-Computer Modeling in Engineering & Sciences, 132(2), 435–450. https://doi.org/10.32604/cmes.2022.020088