摘要
提出了一种改进的粗集属性约简的启发式算法。寻找粗集理论的最小属性约简已经被证明是NP难题,一般采用启发式算法来寻找近似最优解,但求得的约简有冗余属性存在的问题。针对一般启发式算法的不完备性,利用粗集理论中的属性依赖度和属性重要度,构造启发式信息,并在算法中增加了消除冗余的二次约简过程,得到一种改进的启发式算法。最后通过实际算例分析,验证了该改进算法的有效性。
An improved heuristic algorithm of attribute reduction in rough set theory is presented. It has been proved to be a NP - hard problem to search for the minimum attribute set. People usually adopt heuristic algorithm to fmd approximately optimum solution, but there may exit attribute redundance in the obtained attribute reduction. Considering the non - soundness of current heuristic algorithms, with the help of dependence and significance of attribute in Rough Sets, the heuristic information is constructed, and the process of the second reduction is added to the algorithm in order to eliminate the attribute redundance, then an improved heuristic algorithm is obtained. The correctness and effectiveness of the improved algorithm was demonstrated by an experiment.
出处
《电光与控制》
北大核心
2006年第4期46-48,共3页
Electronics Optics & Control
关键词
粗集理论
属性约简
启发式算法
rough set theory
attribute reduction
heuristic algorithm
