Abstract:
The machine learning algorithms play an important role in current spam filter, but a single machine learning algorithm has its own drawback which restrains it from further application in E-mail filter. This paper introduces some typical machine learning algorithms, and constructs a voting E-mail filter model based on multi-machine learning algorithms. The experiments show that this method makes use of every machine learning algorithm’s advantage, and offsets its disadvantage, and achieves better filter performance than a single algorithm.
Key words:
Spam,
Filter,
Machine learning,
Voting
摘要: 机器学习算法在目前垃圾邮件过滤中扮演着重要的角色,但单一学习算法往往有各自的缺陷,限制了其在邮件过滤中的进一步应用。该文介绍了几种典型机器学习算法,并构造了一种基于多机器学习算法的投票式过滤模型。实验表明,该方法充分利用了各机器学习算法的优势,弥补了各自的不足,达到了比单一学习算法更好的过滤性能。
关键词:
垃圾邮件,
过滤,
机器学习,
投票
LI Yongliang; LIU Haiyan; CHEN Jun. Voting E-mail Filter Model Based on Multi-machine Learning Algorithms[J]. Computer Engineering, 2006, 32(19): 214-216.
李永亮;刘海燕;陈 军. 基于多个机器学习算法的投票式邮件过滤模型[J]. 计算机工程, 2006, 32(19): 214-216.