一种基于条件熵的垃圾邮件过滤算法

在垃圾邮件过滤中，针对过滤器对合法邮件的误判问题，提出一种改进的垃圾邮件过滤算法。该算法对信息增益的条件熵估计方法作了改进，结合最小风险贝叶斯决策方法，在英文语料库上进行实验，并采用召回率和正确率对算法进行评价分析。实验结果表明，改进后的方法可提高过滤器对合法邮件的识别能力，降低对合法邮件的误判，减少用户的损失。

A Spam Filtering Algorithm Based on Conditional Entropy

In spare filtering, according to the filter misjudgment for legitimate mails, we put forward an improved spare filtering algorithm, which improves the conditional entropy estimation method of information gain. Combined with the Bayes minimum risk decision method, we analyze the algorithm through the recall and accuracy by carrying out an experiment on the English Corpus. Experimental results show that the improved algorithm can enhance the classification precision and reduce the misjudgment of le- gitimate emails, which can reduce the loss of users.