Evaluation and analysis of feature selection methods for e-mail filtering
.邮件过滤中特征选择方法的性能评价与分析*

The nature of content-based e-mail filtering is a binary text classification problem. Feature selection methods reduced the feature dimension before classifying e-mails in order to reduce the cost of computing and storage, while filtering some noise features to improve the classification accuracy. Feature selection was an important factor which decided the accuracy and timeliness of e-mail filtering. However, every feature selection algorithm had different performance in the same environment, and was affected by classifiers and data distribution. Combining characteristics of e-mail filtering, this paper evaluated and analized the following aspects of feature selection methods which used to filter e-mails: classifier adaptability, data set dependence, time complexity. Experimental results show that odds ratio and document frequency have higher accuracy and less computing time when they are used to filter emails.