实用高效的垃圾邮件过滤算法

为了提高电子邮件中垃圾邮件的过滤准确率和效率,以朴素贝叶斯算法和K最近邻（KNN：K-Nearest Neighbors）算法为基础,对传统垃圾邮件过滤算法进行改进,给出邮件的合法属性和非法属性的概念,并提出一种新的分类算法——基于邮件合法属性和非法属性的分类算法（SEASF：Simple and Efficient Algorithm to Spam Filter based on legitimate attribute and nonlicet attribute）。SEASF计算复杂度较低,可适用于大规模场合及邮件的在线过滤。将SEASF算法应用于垃圾邮件过滤的结果表明,该算法可大幅度提高分类精度,分类速度也令人满意。

Simple and Efficient Algorithm for Spam Filter

In order to improve the precision and efficiency of spam filter.Two new concepts,legitimate attribute and nonlicet attribute,and an improved spam filter algorithm SEASF（Simple and Efficient Algorithm to Spam Filter based on legitimate attribute and nonlicet attribute） based on Naive Bayes algorithm and KNN（K-Nearest Neighbors） algorithm,two traditional spam filter algorithms are proposed.SEASF can be used to filter a large number of specimens and to filter e-mail online,and it is efficient.SEASF is applied to spam filter,the recall and precision are highly improved,and the rate is satisfactory.