A Comparative Study of Classification Based Personal E-mail Filtering

This paper addresses personal E-mail filtering by casting it in the framework of text classification. Modeled as semi-structured documents, Email messages consist of a set of fields with predefined semantics and a number of variable length free-text fields. While most work on classification either concentrates on structured data or free text, the work in this paper deals with both of them. To perform classification, a naive Bayesian classifier was designed and implemented, and a decision tree based classifier was implemented. The design considerations and implementation issues are discussed. Using a relatively large amount of real personal E-mail data, a comprehensive comparative study was conducted using the two classifiers. The importance of different features is reported. Results of other issues related to building an effective personal E-mail classifier are presented and discussed. It is shown that both classifiers can perform filtering with reasonable accuracy. While the decision ...

• Type: Conference
• Year: 2000
• Where: PAKDD
• Authors: Yanlei Diao, Hongjun Lu, Dekai Wu
• Keywords: Data Mining, Naive Bayesian Classifier, PAKDD 2000, Personal E-mail, Personal E-mail Classifier