The Comparison of Chinese Spam Filter Based on Generative Model and Discriminative Model

Yong Han, Yingying Wang, Huafu Ding, Haoliang Qi
{"title":"The Comparison of Chinese Spam Filter Based on Generative Model and Discriminative Model","authors":"Yong Han, Yingying Wang, Huafu Ding, Haoliang Qi","doi":"10.1109/IALP.2011.64","DOIUrl":null,"url":null,"abstract":"Previous studies have shown that discriminative model is better than generative model for spam filtering, which is tested on the English dataset. But the study on Chinese Spam Filter is rare. So we compared the performance of Bogo: a classical generative model, Logistic Regression (LR) and Relaxed Online SVM (ROSVM): two typical discriminative models on the Chinese dataset. Bogo system adopts a generative model, which is based on Bayesian algorithm. We choose the public Chinese datasets: TREC06c, SEWM 2008, SEWM 2010, SEWM 2011, as the test dataset with immediate feedback. The discriminative model gives the better results than the generative model based on spam filter. ROSVM gives the best performance on Chinese spam filter.","PeriodicalId":297167,"journal":{"name":"2011 International Conference on Asian Language Processing","volume":"62 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Asian Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IALP.2011.64","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Previous studies have shown that discriminative model is better than generative model for spam filtering, which is tested on the English dataset. But the study on Chinese Spam Filter is rare. So we compared the performance of Bogo: a classical generative model, Logistic Regression (LR) and Relaxed Online SVM (ROSVM): two typical discriminative models on the Chinese dataset. Bogo system adopts a generative model, which is based on Bayesian algorithm. We choose the public Chinese datasets: TREC06c, SEWM 2008, SEWM 2010, SEWM 2011, as the test dataset with immediate feedback. The discriminative model gives the better results than the generative model based on spam filter. ROSVM gives the best performance on Chinese spam filter.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于生成模型和判别模型的中文垃圾邮件过滤比较
已有研究表明,判别模型比生成模型对垃圾邮件的过滤效果更好,并在英语数据集上进行了测试。但是对中文垃圾邮件过滤的研究却很少。因此,我们比较了Bogo(经典生成模型)、Logistic回归(LR)和放松在线支持向量机(ROSVM)这两种典型判别模型在中文数据集上的性能。Bogo系统采用基于贝叶斯算法的生成模型。我们选择中文公开数据集:TREC06c, SEWM 2008, SEWM 2010, SEWM 2011作为即时反馈的测试数据集。判别模型比基于垃圾邮件过滤的生成模型效果更好。ROSVM在中文垃圾邮件过滤上表现最好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
An Automatic Linguistics Approach for Persian Document Summarization Research on the Uyghur Information Database for Information Processing Research on Multi-document Summarization Model Based on Dynamic Manifold-Ranking Mining Parallel Data from Comparable Corpora via Triangulation A Query Reformulation Model Using Markov Graphic Method
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1