A Method of Spam Filtering Based on Weighted Support Vector Machines

被引：5

作者：

Chen Xiao-li ^{[1
]}

Liu Pei-yu ^{[1
]}

Zhu Zhen-fang ^{[1
]}

Qiu Ye ^{[1
]}

机构：

[1] Shandong Normal Univ, Dept Informat Sci & Engn, Jinan 250014, Peoples R China

来源：

2009 IEEE INTERNATIONAL SYMPOSIUM ON IT IN MEDICINE & EDUCATION, VOLS 1 AND 2, PROCEEDINGS | 2009年

关键词：

D O I：

10.1109/ITIME.2009.5236212

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The problem of content-based spam filtering on machine learning methods actually is a binary classification. SVMs can separate the data into two categories optimally so SVMs suit to spam filtering. With used into spam filtering, the standard support vector machine involves the minimization of the error function and the accuracy of the SVM is very high, but the degree of misclassification of legitimate emails is high. In order to solve that problem, this paper proposed a method of spam filtering based on weighted support vector machines. Experimental results show that the algorithm can enhance the filtering performance effectively.

引用

页码：947 / 950

页数：4

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