Ham or spam? A comparative study for some content-based classification algorithms for email filtering

Abstract

Spam emails are widely spreading to constitute a significant share of everyone's daily inbox. Being a source of financial loss and inconvenience for the recipients, spam emails have to be filtered and separated from legitimate ones. This paper presents a survey of some popular filtering algorithms that rely on text classification to decide whether an email is unsolicited or not. A comparison among them is performed on the SpamBase dataset to identify the best classification algorithm in terms of accuracy, computational time, and precision/recall rates.