【摘要】 提出了基于粗糙集理论和贝叶斯分类算法的垃圾邮件过滤方法。利用粗糙集约简算法对邮件样本集进行特征约简,删除对邮件过滤结果影响不大的冗余特征,从而降低了输入样本集的维数,解决了贝叶斯分类器训练时间长,样本集占用的存储空间过大的问题。实验证明,该方法可以提高邮件过滤的准确性和训练的速度。更多还原

【Abstract】 This paper proposed a spam filtering method based on Rough set theory and Bayesian classifier algorithm.Then the amount of features are reduced by deleting redundant features with little significance on filtering effect based on rough set theory,resulting in a input sample with reduced number of dimension.Using this method,it can overcome the shortages of Bayies classifier-time-consuming of training and massive dataset storage.Experiments proved that this mechanism could greatly boost both the system’s accuracy and the training speed.更多还原