@Article{cmes.2022.020088,
AUTHOR = {Adel Hamdan Mohammad , Sami Smadi, Tariq Alwada’n},
TITLE = {Email Filtering Using Hybrid Feature Selection Model},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {132},
YEAR = {2022},
NUMBER = {2},
PAGES = {435--450},
URL = {http://www.techscience.com/CMES/v132n2/48307},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2022.020088}
}