IE interfaces (산업공학)

Korean Institute of Industrial Engineers (대한산업공학회)
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Performance Comparison of Naive Bayesian Learning and Centroid-Based Classification for e-Mail Classification

전자메일 분류를 위한 나이브 베이지안 학습과 중심점 기반 분류의 성능 비교

  • Kim, Kuk-Pyo (Department of Industrial & Systems Engineering, Dongguk University) ;
  • Kwon, Young-S. (Department of Industrial & Systems Engineering, Dongguk University)
  • 김국표 (동국대학교 산업시스템공학부) ;
  • 권영식 (동국대학교 산업시스템공학부)
  • Received : 2003.10.08
  • Accepted : 2004.12.10
  • Published : 2005.03.31
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Abstract

With the increasing proliferation of World Wide Web, electronic mail systems have become very widely used communication tools. Researches on e-mail classification have been very important in that e-mail classification system is a major engine for e-mail response management systems which mine unstructured e-mail messages and automatically categorize them. In this research we compare the performance of Naive Bayesian learning and Centroid-Based Classification using the different data set of an on-line shopping mall and a credit card company. We analyze which method performs better under which conditions. We compared classification accuracy of them which depends on structure and size of train set and increasing numbers of class. The experimental results indicate that Naive Bayesian learning performs better, while Centroid-Based Classification is more robust in terms of classification accuracy.

Keywords

References

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