摘要
以朴素贝叶斯理论作为基石并结合信息增益、代价因子等方法,尝试设计一种基于用户需求的垃圾邮件过滤分类模型,在垃圾邮件一次分类基础上提出邮件先过滤后分类方法,进而改进传统邮件分类一次阈值比较,变为两次阈值比较,且应用反馈技术以应对垃圾邮件的日益变化。该模型可能会对垃圾邮件多分类研究具有一定的参考价值。
The paper designs a spam filtering and classification model based on user needs combined with information gain and cost factor. At the same time, it puts forward a filtration & classification method, which replaces one threshold comparison with two threshold comparisons while the traditional spam system filter e-mail only once. Besides, a feedback technique is applied to update training set in response to the ever-changing spam. The model might also have some reference value to spam multi- classification study.
出处
《信息技术》
2013年第12期90-94,共5页
Information Technology
关键词
朴素贝叶斯
信息增益
代价因子
用户需求
过滤分类
Naive Bayesian
information gain
cost factor
user needs
filtration & classification