Model For Email Spam Classification Using Hybrid Machine Learning Technique
DOI:
https://doi.org/10.26438/ijcse/v13i1.2432Keywords:
Email Spam, Machine Learning, Genetic Algorithm, Particle Swarm OptimizationAbstract
An optimized Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) together (GA-PSO) method for email spam classification is presented in this paper. To improve classification accuracy and computing efficiency, the model combines The collective intelligence found in Particle Swarm Optimization (PSO).with the evolutionary powers of Genetic Algorithms (GA). The proposed GA-PSO classifier was rigorously tested over 400 cycles using datasets from Enron and Spam Assassin. Superior performance measures were attained by the model, including a 50% improvement in fitness margin, a 3% decrease in fitness error margin, and a computational efficiency that was five times faster than traditional techniques. By developing a strong, scalable algorithm with enhanced decision-making accuracy, this research advances spam detection and makes a substantial advancement in tackling email spam issues.
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