不良网页过滤是一种两类网页分类问题。提出了一种基于粗糙集与贝叶斯决策相结合的不良网页分类过滤方法,首先利用粗糙集理论的区分矩阵和区分函数得到网页分类决策的属性约简;然后通过贝叶斯决策理论对网页进行分类与过滤决策。仿真实验表明,该方法在不良网页分类过滤系统中开销小,过滤准确度高,因而在快速过滤不良网页的应用中具有工程应用价值。

Treating the webpage filtering as a classification task, a new method based on Rough set and Bayesian decision theory is proposed. Attribute reduction of Webpages classification is obtained by the discernibility matrix and discernibility function according to the the Rough Set theory. Then, the Webpage is classified and filtered by the Bayesian decision theory. Simulation experiments show the effectiveness of the proposed method.
Key wordsinformation security, Webpage filtering, rough set, discernibility matrix, Bayesian decision