摘要
属性约简是粗糙集理论研究的核心问题之一,而且现已证明寻找一个决策表的最小约简是NP-hard问题。针对变精度粗糙集理论的属性约简问题,从相对可辨识矩阵,属性的重要度作为启发式的信息,给出变精度粗糙集的属性约简算法的改进,在一定程度上简化了属性约简的计算,提高了属性约简的效率。最后通过实例证明了改进的算法给出信息系统的属性约简结果的正确性。
Attribute reduction is one of the key topics in the Rough Set theory field. It has been proved that computing the minimal reduction of decision table is an NP- hard problem. Relative discernibility matrix and attribute significance are considered to solve the attribute reduction of variable precision rough sets theory. Moreover, the improved algorithm of attribution reduction reduction is given. It can simplify the operation and enhance reduction the efficiency of seeking the reduction in some extent. At last, a practical example is given to show the validity of the algorithm.
出处
《计算机技术与发展》
2009年第7期35-37,共3页
Computer Technology and Development
基金
安徽省自然科学基金项目(050420204)
关键词
属性约简
变精度粗糙集
相对差异矩阵
属性重要度
attribute reduction
variable precision rough set relative discernibility Matrix
attribute significance
