基于朴素贝叶斯和支持向量机的自适应垃圾短信过滤系统  

Spam message self-adaptive filtering system based on Nave Bayes and support vector machine

作　　者：金展[1] 范晶[1] 陈峰[2] 徐从富[1] 

机构地区：[1]浙江大学计算机科学与技术学院,杭州310027 [2]浙江省公安厅科技处,杭州310009

出　　处：《计算机应用》2008年第3期714-718,共5页journal of Computer Applications

基　　金：国家863计划项目(2007AA01Z197);国家自然科学基金资助项目(60402010)

摘　　要：随着短信业务的不断发展,垃圾短信的特征和内容也在不断变化,传统垃圾短信过滤系统中存在的主要问题是,短信特征和内容未能得到及时更新而导致过滤性能降低。考虑朴素贝叶斯的快速统计分类及支持向量机(SVM)的增量训练等特点,将其应用于垃圾短信过滤中,并把分析结果及时反馈给在线过滤子系统,使得系统具有更好的自适应性。实验结果表明,该方法可有效地解决当前垃圾短信过滤系统中存在的问题。With the development of the short message services, the characteristics and contents of the spare short Message are also changing constantly, the main problems that exist in the traditional short message filtering systems are that the characteristics and contents fail to be updated in time, which reduced the filter capability. This paper mainly utilized Nave Bays advantage of rapid statistics classification and Support Vector Machine （SVM） incremental training characteristic in Spam Short Message filtering, and provided feedbacks to the online filtering sub-system in time in order to enhance the system-s selfadaptabihty. The experimental results show that this new method effectively deals with the above problems in the traditional spain short Message filtering systems,

关 键 词：垃圾短信过滤 朴素贝叶斯 支持向量机 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP302.1[自动化与计算机技术—控制科学与工程]