Survey of machine learning methods for spam e-mail classification (original) (raw)

The humongous volume of unsolicited bulk e-mail (spam) which is further increasing, is the major cause for developing antispam protection filters. Machine learning provides a very optimized approach to automatically filter spams at a very successful rate. Here, in this paper we survey some of the most popular machine learning algorithms (Naïve Bayes, k-NN, SVMs and ANN) and their applicability to the problem of spam e-mail classification. Descriptions of the algorithms are presented, and the comparison of their performance on the UCI spam-base dataset is presented. Keywords⸻ Spam, E-mail classification, Machine learning algorithms, k-NN, SVM, Naïve Bayes, ANN.