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垃圾邮件分类的偏依赖特性研究 被引量：1

Research on the Characteristic of Partial Dependency for Spam Classification
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摘要 由于相对于漏报,误报会对邮件过滤性能造成更负面的影响,因此有必要研究如何让邮件过滤器对误报代价表现出更高的敏感性.本文通过引入具有偏依赖特征的权值系数函数,提出了一种能够实现非对称训练学习的改进拟合Logistic Regression邮件分类算法模型.根据在实际邮件样本集上所作测试试验,在分类精度性能没有降低的条件下,验证了新分类模型在误报率和漏报率两项指标之间存在较明显的偏依赖特性,同时对扰动特征数据表现出较强鲁棒特性. Since false positive, compared with false negative, would cause much higher negative influence on email filter＇ s performance,it is necessary to investigate how to make the email filter become more sensitive to handle the cost of false positive. This paper brings forward an advanced fitting Logistic Regression model for spam discrimination by inn：educing a coefficientweighted function which can help to implement unbalanced classifier training. Without performance degradation on classification precision, the results of the performance evaluation on actual email testing sets verify that the new categorization model is of the partial dependent characteristic evidently between the criteria of false positive ratio and false negative ratio. Meanwhile, the testing results suggest that the model is robust to perturbing data as well.
作者 刘震 谭良 周明天
机构地区 电子科技大学计算机学院卫士通安全联合实验室
出处 《电子学报》 EI CAS CSCD 北大核心 2007年第10期1870-1874,共5页 Acta Electronica Sinica
基金 国家863高技术研究发展计划(No.863-104-03-01)
关键词 垃圾邮件 偏依赖 误报率 漏报率 spam characteristic of partial dependency false positive ratio false negative ratio
分类号 TP393 [自动化与计算机技术—计算机应用技术]
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参考文献14

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二级参考文献7

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共引文献11

同被引文献7

  • 1Niu Yuan.A quantitative study of forum spamming using contextbased analysis[C]//Proeeedings of the 14th Annual Network and Distributed System Security Symposium,San Diego,CA,2007:79-92.
  • 2Mishne G,Carmel D.Blocking blog spam with language model disagreement[C]//Proceedings of the 1st AIRWeb.New York:ACM, 2005 : 1-6.
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  • 4Lin Yu-ru.Splog detection using self-similarity analysis on blog temporal dynamics[C]//Proceedings of AIRWeb 2007.New York: ACM, 2007 : 1-8.
  • 5Brooks C H,Montanez N.Improved annotation of the blogosphere via autotagging and hierarchical clustering[C]//Proceedings of the 15th International Conference on World Wide Web.New York: ACM, 2006 : 625-632.
  • 6Lin C J,Weng R C,Keerthi S S.Trust region newton methods for large-scale logistic regression[C]//Proceedings of the 24th International Conference on Machine Learning.New York:ACM,2007: 561-568.
  • 7代六玲,黄河燕,陈肇雄.中文文本分类中特征抽取方法的比较研究[J].中文信息学报,2004,18(1):26-32. 被引量：230

引证文献1

二级引证文献11

电子学报
2007年 第10期

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