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Improved Email Classification through Enriched Feature Space

  • Yunming Ye
  • Fanyuan Ma
  • Hongqiang Rong
  • Joshua Zhexue Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3129)

Abstract

This paper presents a novel feature space enriching (FSE) technique to address the problem of sparse and noisy feature space in email classification. The (FSE) technique employs two semantic knowledge bases to enrich the original sparse feature space, which results in more semantic-richer features. From the enriched feature space, the classification algorithms can learn improved classifiers. Naive Bayes and support vector machine are selected as the classification algorithms. Experiments on an enterprise email dataset have shown that the FSE technique is effective for improving the email classification performance.

Keywords

Email Classification Feature Space Enriching Semantic Knowledge Base Text Categorization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Yunming Ye
    • 1
  • Fanyuan Ma
    • 1
  • Hongqiang Rong
    • 2
  • Joshua Zhexue Huang
    • 2
  1. 1.Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.E-Business Technology InstituteThe University of Hong KongHong KongChina

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