一种基于位向量交集运算的规则冲突检测算法

无论从报文分类算法自身还是从安全角度,规则冲突检测都是一个重要的研究课题.而目前常用的冲突检测算法效率较低.针对这一情况,在ASBV算法基础之上,提出了一种高效的冲突检测算法DBBV.同ASBV算法类似,DBBV算法也采用了分治思想和位向量技术.但与ASBV算法不同,在每一维规则分量处理过程中,DBBV算法只需要进行一次位向量交集运算,而ASBV算法需要进行多次位向量并集运算;DBBV算法支持以范围形式表示的规则集,而ASBV算法只支持以前缀形式表示的规则集.对DBBV算法的正确性进行了证明,测试表明其检测速度快于ASBV算法.

基于动态极大度的极小碰集求解方法

在计算集合簇的碰集时,结合SE-Tree(set enumeration tree)形式化地表达计算过程,逐步生成所有的极小碰集.并在SE-Tree中添加了终止结点,避免了非极小碰集的产生,并且不会因剪枝而丢失正确的解.提出未扩展元素度的概念和结点度的概念,进而在扩展SE-Tree结点时按照未扩展元素度由大到小的顺序扩展,极早地生成集合簇的碰集,减少枚举树生成的结点个数,并且直接根据结点度得出结点对应的集合是否为集合簇的碰集,避免计算集合是否为集合簇的碰集.实验结果表明,该算法程序容易编制且效率较好.

Feature study for improving Chinese overlapping ambiguity resolution based on SVM

In order to improve Chinese overlapping ambiguity resolution based on a support vector machine, statistical features are studied for representing the feature vectors. First, four statistical parameters-mutual information, accessor variety, two-character word frequency and single-character word frequency are used to describe the feature vectors respectively. Then other parameters are tried to add as complementary features to the parameters which obtain the best results for further improving the classification performance. Experimental results show that features represented by mutual information, single-character word frequency and accessor variety can obtain an optimum result of 94. 39%. Compared with a commonly used word probability model, the accuracy has been improved by 6. 62%. Such comparative results confirm that the classification performance can be improved by feature selection and representation.