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

Yong Han, Yingying Wang, Huafu Ding, Haoliang Qi
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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.
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。
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