摘要
为了获得决策系统中更好的相对属性约简,本文提出了一种基于差别矩阵的启发式属性约简算法。该算法以求差别矩阵为基础,不仅考虑了所选择条件属性与决策属性的互信息,还考虑了其取值的分布情况,从信息论角度定义了一种新的属性重要性度量方法,将其作为启发式信息,最终求得属性约简集。实例表明,算法能够有效地对决策系统进行约简,获得比较理想的约简结果,同时约简后的决策规则数目较少。
In order to obtain good relative attribute reduction m decision systems, a heuristic aigorithm for attribute reduction based on discernibility matrix is proposed. The algorithm is based on the discernibility matrix, not only the mutual information between selected conditional attributes and decision attributes are considered, but also its value distribution. A new attribute importance measurement method is defined from the viewpoint of information theory, and the measurement is used as the heuristic information. Finally an attribute reduction set is obtained. The experimental results show that the algorithm can effectively reduce the decision system and obtain ideal reduction results, and that the number of decision rules after the reduction is small.
出处
《计算机工程与科学》
CSCD
2008年第6期73-75,共3页
Computer Engineering & Science
基金
国家自然科学基金资助项目(60674026)
关键词
粗糙集
差别矩阵
属性约简
互信息
rough set
discernibility matrix
attribute reduction
mutual information
