Detecting Spam Email With Machine Learning Optimized With Bio-Inspired Metaheuristic Algorithms
Electronic mail has eased communication methods for many organisations as well as individuals. This method is exploited for fraudulent gain by spammers through sending unsolicited emails. This article aims to present a method for detection of spam emails with machine learning algorithms that are optimized with bio-inspired methods. A literature review is carried to explore the efficient methods applied on different datasets to achieve good results. An extensive research was done to implement machine learning models using Naïve Bayes, Support Vector Machine, Random Forest, Decision Tree and Multi-Layer Perceptron on seven different email datasets, along with feature extraction and pre-processing. The bio-inspired algorithms like Particle Swarm Optimization and Genetic Algorithm were implemented to optimize the performance of classifiers. Multinomial Naïve Bayes with Genetic Algorithm performed the best overall. The comparison of our results with other machine learning and bio-inspired models to show the best suitable model is also discussed.
PROJECT OUTPUT VIDEO:
System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 2 GB
Operating system : Windows 10.
Coding Language : Python
SIMRAN GIBSON, BIJU ISSAC, LI ZHANG, AND SEIBU MARY JACOB, “Detecting Spam Email With Machine Learning Optimized With Bio-Inspired Metaheuristic Algorithms”, IEEE ACCESS, 2020.
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