Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology

Research On Spam Filter Based On Improved Naive Bayes and KNN Algorithm

Authors
Biyi Ren, Yuliang Shi
Corresponding Author
Biyi Ren
Available Online March 2016.
DOI
10.2991/icmmct-16.2016.220How to use a DOI?
Keywords
spam filter, Naive Bayes, KNN.
Abstract

In the field of data mining and pattern recognition, classification is a very important core technology. This paper present two kinds of improved classification algorithm. Using the improved Naive Bayes (NB) and KNN algorithm structure classifier to filter normal mail and spam. Improved NB algorithm can dynamically adjust the threshold k, reduces the mail mistake rate. Center vector method is introduced into the similarity calculation formula of KNN, better reflect the interrelation between the text and categories. Finally, improved NB algorithm and KNN algorithm make comparison and analysis, it is concluded that the effective experimental results.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology
Series
Advances in Engineering Research
Publication Date
March 2016
ISBN
978-94-6252-165-0
ISSN
2352-5401
DOI
10.2991/icmmct-16.2016.220How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Biyi Ren
AU  - Yuliang Shi
PY  - 2016/03
DA  - 2016/03
TI  - Research On Spam Filter Based On Improved Naive Bayes and KNN Algorithm
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology
PB  - Atlantis Press
SP  - 1112
EP  - 1115
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-16.2016.220
DO  - 10.2991/icmmct-16.2016.220
ID  - Ren2016/03
ER  -