Optimization of Naïve Bayes Classifier for Spam E-Mail Detection - Shayan Mondal, P. Vidya Sagar, Suneetha Bulla, Sai Charan Lanka, Kodali Pujita, Kommana Akhila (International Journal of Intelligent Systems and Applications in Engineering, 2024)
: E-Mail, a popular and official communication platform, widely used method of communication that facilitates exchange of information between individuals or organizations in a convenient and efficient way to send and receive any kind of information instantly from any corner of the world. But, due to this drastic growth of the usage of E-Mail, spammers are using this platform to perform frauds through mails that mails are known as Spam Mails. Spam Mails can be detected and identified using various approaches. Among those approaches Machine Learning is widely used. In Machine Learning, Naïve Bayes Classifier stands out with the highest accuracy this is due to “Low False Positive Error Rate”. Although Naïve Bayes Classifier gives us the best accuracy among all other Machine Learning models, we can still optimize it to give a better accuracy