본문 바로가기
[학술저널]

  • 학술저널

Jongwan Kim(대구대학교)

표지

북마크 0

리뷰 0

이용수 28

피인용수 0

초록

Conventional filters using email header and body information equally judge whether an incoming email is spam or not. However this is unrealistic in everyday life because each person has different criteria to judge what is spam or not. To resolve this problem, we consider user preference information as well as email category information derived from the email content. In this paper, we have developed a personalized anti-spam system using ontologies constructed from rules derived in a data mining process. The reason why traditional content-based filters are not applicable to the proposed experimental situation is described. In also, several experiments constructing classifiers to decide email category and comparing classification rule learners are performed. Especially, an ID3 decision tree algorithm improved the overall accuracy around 17% compared to a conventional SVM text miner on the decision of email category. Some discussions about the axioms generated from the experimental dataset are given too.

목차

ABSTRACT
1. INTRODUCTION
2. PROPOSED SYSTEM
3. EXPERIMENTS AND DISCUSSION
4. CONCLUSION
ACKNOWLEDGEMENTS
REFERENCES

참고문헌(0)

리뷰(0)

도움이 되었어요.0

도움이 안되었어요.0

첫 리뷰를 남겨주세요.
Insert title here