This paper presents a feature selection algorithm based on particle swarm optimization (PSO), which decreases dimensionality and improves the accuracy of spam ...
Experimental results show that the PSO-based feature selection algorithm was presented to generate excellent feature selection results with the minimal set ...
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This paper investigates how particle swarm optimization algorithm can help select features relevant for spam email classification and shows that the ...
Experimental results show that this method is able to detect not only known attacks, but also new and unknown attacks. Another example is [135] which again uses ...
New email spam detection model based on negative selection algorithm and particle swarm optimization (NSA–PSO) is implemented.
This paper proposes Swarm based hybrid technique CFS-PSO, which combines the characteristics of Correlation Based Feature Selection Technique (CFS) and ...
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In this paper, we propose a spam filtering approach consisted of two main stages; feature selection and emails classification. In the first step a Particle ...
ABSTRACT. In email spam detection, not only different parts and content of emails are important, but also the structural and special features of these ...
it with our PSO-based method of spam detection to attain the acceptable results of 71/8 percent in spam detection rate. As future work, using more features ...
In this paper, we proposed a novel spam detection method that focused on reducing the false positive error of mislabeling nonspam as spam.