Spam Filter Based on Term Co-Occurrence Model
Fei Xie · Zhongwen xinxi xuebao · 2009
The aim of spam filtering is to distinguish the spam and the ham.The traditional methods used vector space model and feature selection approaches to extract features representing the contents of emails.However,these methods do not take the semantic information among words into account.In this paper,a new method is proposed to extract email features by combining the vector space model and the term co-occurrence.The covering algorithm is then employed to classify emails.Experiments show that the proposed method significantly improves the filtering performances compared with traditional ones.The features selected by utilizing term co-occurrence model are more representative than those chosen by the vector space model.