Application of Support Vector Machine Algorithm in E Mail Spam Filtering
The problem of spam classification has received broad and current interest in the recent years. One of the most common supervised learning methods applied for binary text classification is Naive Bayes (NB) algorithm. We propose Support Vector Machine (SVM) spam filtering algorithm, treating NB as a baseline. Taking into account SVMs ability to separate features in high dimensional space, we aim to prove the hypothesis stating that SVM is more effective than NB regarding the spam filtering problem.