Research of a Spam Filter based on Improved Naive Bayes

DOI

10.2991/icadme-16.2016.96How to use a DOI?

Keywords

Naive Bayes,SVM,spam mail,trim

Abstract

In spam filtering filed, Naive Bayes algorithm is one of the most popular algorithms, but its conditional independent assumption makes its reliance on training sets of sample space distribution. In order to improve the accuracy rate, sample space became so complex, resulting in the algorithm of time complexity is increased and the internal stability is poor. In order to solve above problems, this paper proposes a modified using support vector machine(SVM) of the Native Bayes algorithm :SVM-NB.First,SVM constructs an optimal separating hyperplane for training set in the sample space at the junction two types of collection then according to its similarities and differences between the neighboring class mark for each sample to reduce the sample space also increase the independence of classes of each samples,finally using Naive Bayesian classification algorithm for mails. The simulation results show that the algorithm reduces the sample space complexity, fast to get the optimal classification feature subset,effectively improve the classification speed and accuracy of spam filtering.

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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TY  - CONF
AU  - Ye Yuan
AU  - Shouzheng Li
AU  - Yuanyuan Wang
AU  - Chao Liu
AU  - Weimiao Feng
AU  - Min Yu
PY  - 2017/07
DA  - 2017/07
TI  - Research of a Spam Filter based on Improved Naive Bayes
BT  - Proceedings of the 2016 6th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2017)
PB  - Atlantis Press
SP  - 555
EP  - 559
SN  - 2352-5401
UR  - https://doi.org/10.2991/icadme-16.2016.96
DO  - 10.2991/icadme-16.2016.96
ID  - Yuan2017/07
ER  -