基于粗糙集的CBR系统属性约简改进算法

提出一种基于粗糙集的改进的约简算法和决策表预处理方法，在对决策表进行预处理后，以核为基础，用改进的加权属性重要度方法选择非核属性，增加反向删除操作。该算法能够充分反映专家经验知识，保证得到一个Pawlak约简，大幅度提高案例的检索效率。运用Matlab编程实现了该算法，通过实例对算法进行分析、对比，证明了其正确性和有效性。

Improved Attribute Reduction Algorithm for Case-Based Reasoning System Based on Rough Set

This paper proposes an algorithm based on improved rough set and a pretreatment method for decision table. The algorithm does the attribute reduction of decision table based on the core attribute after pretreating the decision table, and uses the method of improved weighted mean attribute significance to select the non-core attribute. It adds the converse eliminate action to ensure that it can get a Pawlak reduct, so as to improve the efficiency of case searching. Using Matlab to implement the algorithm, the running result shows the validity and veracity of the method by analyzing and comparing algorithm through an example.