A Comparative Study of Classification Based Personal E-mail Filtering

  • Yanlei Diao
  • Hongjun Lu
  • Dekai Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1805)


This paper addresses personal E-mail filtering by casting it in the framework of text classification. Modeled as semi-structured documents, E-mail 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 tree based classifier outperforms the Bayesian classifier when features and training size are selected optimally for both, a carefully designed naive Bayesian classifier is more robust.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Yanlei Diao
    • 1
  • Hongjun Lu
    • 1
  • Dekai Wu
    • 1
  1. 1.Department of Computer ScienceThe Hong Kong University of Science and TechnologyKowloonHong Kong

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